#softwaredocumentation — Public Fediverse posts

"If you are thinking about using an AI agent for documentation, here is what I think matters most.

Teach the agent, do not just instruct it. A prompt that says "write documentation for this feature" produces generic content. A skill that defines your voice, your formatting rules, your page structure, and your verification checklist produces documentation that sounds like your team wrote it. The upfront investment in the skill pays off on every subsequent page.

Make screenshots reproducible. Manual screenshots are the first thing that goes stale. A declarative manifest that can regenerate every screenshot in one command is worth the engineering effort. It changes screenshots from a one-time cost to a maintained artifact.

Phase your work. Even if you are using an agent, "write all the docs" is not a plan. Break it into phases with clear scope and clear deliverables. This gives you stopping points, review points, and the ability to course-correct.

Expect things to break. OCR will misread text. The UI will change mid-sprint. Preview URLs will go stale. The difference between a frustrating experience and a productive one is whether you encode the fix into a skill so it never happens again.

Review everything. The agent does not replace your judgment. It replaces the mechanical work. You still need to read every page, check every screenshot, and verify that the documentation matches what the user actually sees. The agent writes the first draft. You make it right."

https://dev.to/debs_obrien/how-i-documented-an-entire-product-in-4-days-with-an-ai-agent-3338

#TechnicalWriting #SoftwareDocumentation #AI #GenerativeAI #AIAgents #AgenticAI #LLMs

"If you are thinking about using an AI agent for documentation, here is what I think matters most.

Teach the agent, do not just instruct it. A prompt that says "write documentation for this feature" produces generic content. A skill that defines your voice, your formatting rules, your page structure, and your verification checklist produces documentation that sounds like your team wrote it. The upfront investment in the skill pays off on every subsequent page.

Make screenshots reproducible. Manual screenshots are the first thing that goes stale. A declarative manifest that can regenerate every screenshot in one command is worth the engineering effort. It changes screenshots from a one-time cost to a maintained artifact.

Phase your work. Even if you are using an agent, "write all the docs" is not a plan. Break it into phases with clear scope and clear deliverables. This gives you stopping points, review points, and the ability to course-correct.

Expect things to break. OCR will misread text. The UI will change mid-sprint. Preview URLs will go stale. The difference between a frustrating experience and a productive one is whether you encode the fix into a skill so it never happens again.

Review everything. The agent does not replace your judgment. It replaces the mechanical work. You still need to read every page, check every screenshot, and verify that the documentation matches what the user actually sees. The agent writes the first draft. You make it right."

https://dev.to/debs_obrien/how-i-documented-an-entire-product-in-4-days-with-an-ai-agent-3338

#TechnicalWriting #SoftwareDocumentation #AI #GenerativeAI #AIAgents #AgenticAI #LLMs

"If you are thinking about using an AI agent for documentation, here is what I think matters most.

Teach the agent, do not just instruct it. A prompt that says "write documentation for this feature" produces generic content. A skill that defines your voice, your formatting rules, your page structure, and your verification checklist produces documentation that sounds like your team wrote it. The upfront investment in the skill pays off on every subsequent page.

Make screenshots reproducible. Manual screenshots are the first thing that goes stale. A declarative manifest that can regenerate every screenshot in one command is worth the engineering effort. It changes screenshots from a one-time cost to a maintained artifact.

Phase your work. Even if you are using an agent, "write all the docs" is not a plan. Break it into phases with clear scope and clear deliverables. This gives you stopping points, review points, and the ability to course-correct.

Expect things to break. OCR will misread text. The UI will change mid-sprint. Preview URLs will go stale. The difference between a frustrating experience and a productive one is whether you encode the fix into a skill so it never happens again.

Review everything. The agent does not replace your judgment. It replaces the mechanical work. You still need to read every page, check every screenshot, and verify that the documentation matches what the user actually sees. The agent writes the first draft. You make it right."

https://dev.to/debs_obrien/how-i-documented-an-entire-product-in-4-days-with-an-ai-agent-3338

#TechnicalWriting #SoftwareDocumentation #AI #GenerativeAI #AIAgents #AgenticAI #LLMs

"If you are thinking about using an AI agent for documentation, here is what I think matters most.

Teach the agent, do not just instruct it. A prompt that says "write documentation for this feature" produces generic content. A skill that defines your voice, your formatting rules, your page structure, and your verification checklist produces documentation that sounds like your team wrote it. The upfront investment in the skill pays off on every subsequent page.

Make screenshots reproducible. Manual screenshots are the first thing that goes stale. A declarative manifest that can regenerate every screenshot in one command is worth the engineering effort. It changes screenshots from a one-time cost to a maintained artifact.

Phase your work. Even if you are using an agent, "write all the docs" is not a plan. Break it into phases with clear scope and clear deliverables. This gives you stopping points, review points, and the ability to course-correct.

Expect things to break. OCR will misread text. The UI will change mid-sprint. Preview URLs will go stale. The difference between a frustrating experience and a productive one is whether you encode the fix into a skill so it never happens again.

Review everything. The agent does not replace your judgment. It replaces the mechanical work. You still need to read every page, check every screenshot, and verify that the documentation matches what the user actually sees. The agent writes the first draft. You make it right."

https://dev.to/debs_obrien/how-i-documented-an-entire-product-in-4-days-with-an-ai-agent-3338

#TechnicalWriting #SoftwareDocumentation #AI #GenerativeAI #AIAgents #AgenticAI #LLMs

"If you are thinking about using an AI agent for documentation, here is what I think matters most.

Teach the agent, do not just instruct it. A prompt that says "write documentation for this feature" produces generic content. A skill that defines your voice, your formatting rules, your page structure, and your verification checklist produces documentation that sounds like your team wrote it. The upfront investment in the skill pays off on every subsequent page.

Make screenshots reproducible. Manual screenshots are the first thing that goes stale. A declarative manifest that can regenerate every screenshot in one command is worth the engineering effort. It changes screenshots from a one-time cost to a maintained artifact.

Phase your work. Even if you are using an agent, "write all the docs" is not a plan. Break it into phases with clear scope and clear deliverables. This gives you stopping points, review points, and the ability to course-correct.

Expect things to break. OCR will misread text. The UI will change mid-sprint. Preview URLs will go stale. The difference between a frustrating experience and a productive one is whether you encode the fix into a skill so it never happens again.

Review everything. The agent does not replace your judgment. It replaces the mechanical work. You still need to read every page, check every screenshot, and verify that the documentation matches what the user actually sees. The agent writes the first draft. You make it right."

https://dev.to/debs_obrien/how-i-documented-an-entire-product-in-4-days-with-an-ai-agent-3338

#TechnicalWriting #SoftwareDocumentation #AI #GenerativeAI #AIAgents #AgenticAI #LLMs

"The future of enterprise technical documentation will not belong to organizations that merely generate more content with AI. It will belong to organizations that build semantically governed, operationally validated, and explainable knowledge ecosystems around AI generation.

Large language models are remarkable language-generation systems, but they remain fundamentally probabilistic, and no amount of vector-based probabilistic augmentation, recursive prompt gymnastics, or trillions of additional parameters magically transforms probabilistic token prediction into deterministic operational intelligence — regardless of what the AI snake-oil salesmen on LinkedIn insist between inspirational rocket-ship emojis. LLMs predict statistically likely outputs. They do not inherently understand operational correctness, governance policy, procedural safety, rollback integrity, regulatory compliance, or whether the “helpful” configuration change they just suggested is going to quietly detonate a production Kubernetes cluster at 2:13 a.m. while everyone is asleep and the on-call engineer is reconsidering their career choices.

That is not a moral failure of AI. It is simply the architectural reality of probabilistic systems pretending to perform deterministic operational reasoning often enough to make people dangerously optimistic.

This is precisely why deterministic models and governance matter.

Structured content, semantic markup, metadata governance, provenance tracking, DOM Graph RAG, iiRDS frameworks, knowledge graphs, RDF and OWL ontologies, context graphs, deterministic inference engines, orchestration platforms, Docs-as-Tests automation, and runtime observability together create something fundamentally different from prompt engineering. They create governed operational ecosystems capable of supporting trustworthy enterprise AI at scale."

https://medium.com/@nc_mike/deterministic-and-agentic-ai-architectures-for-technical-documentation-3fb2956a1334

#AI #GenerativeAI #DocsAsTests #LLMs #AgenticAI #DITAXML #AIAgents #TechnicalWriting #SoftwareDocumentation

"The future of enterprise technical documentation will not belong to organizations that merely generate more content with AI. It will belong to organizations that build semantically governed, operationally validated, and explainable knowledge ecosystems around AI generation.

Large language models are remarkable language-generation systems, but they remain fundamentally probabilistic, and no amount of vector-based probabilistic augmentation, recursive prompt gymnastics, or trillions of additional parameters magically transforms probabilistic token prediction into deterministic operational intelligence — regardless of what the AI snake-oil salesmen on LinkedIn insist between inspirational rocket-ship emojis. LLMs predict statistically likely outputs. They do not inherently understand operational correctness, governance policy, procedural safety, rollback integrity, regulatory compliance, or whether the “helpful” configuration change they just suggested is going to quietly detonate a production Kubernetes cluster at 2:13 a.m. while everyone is asleep and the on-call engineer is reconsidering their career choices.

That is not a moral failure of AI. It is simply the architectural reality of probabilistic systems pretending to perform deterministic operational reasoning often enough to make people dangerously optimistic.

This is precisely why deterministic models and governance matter.

Structured content, semantic markup, metadata governance, provenance tracking, DOM Graph RAG, iiRDS frameworks, knowledge graphs, RDF and OWL ontologies, context graphs, deterministic inference engines, orchestration platforms, Docs-as-Tests automation, and runtime observability together create something fundamentally different from prompt engineering. They create governed operational ecosystems capable of supporting trustworthy enterprise AI at scale."

https://medium.com/@nc_mike/deterministic-and-agentic-ai-architectures-for-technical-documentation-3fb2956a1334

#AI #GenerativeAI #DocsAsTests #LLMs #AgenticAI #DITAXML #AIAgents #TechnicalWriting #SoftwareDocumentation

"The future of enterprise technical documentation will not belong to organizations that merely generate more content with AI. It will belong to organizations that build semantically governed, operationally validated, and explainable knowledge ecosystems around AI generation.

Large language models are remarkable language-generation systems, but they remain fundamentally probabilistic, and no amount of vector-based probabilistic augmentation, recursive prompt gymnastics, or trillions of additional parameters magically transforms probabilistic token prediction into deterministic operational intelligence — regardless of what the AI snake-oil salesmen on LinkedIn insist between inspirational rocket-ship emojis. LLMs predict statistically likely outputs. They do not inherently understand operational correctness, governance policy, procedural safety, rollback integrity, regulatory compliance, or whether the “helpful” configuration change they just suggested is going to quietly detonate a production Kubernetes cluster at 2:13 a.m. while everyone is asleep and the on-call engineer is reconsidering their career choices.

That is not a moral failure of AI. It is simply the architectural reality of probabilistic systems pretending to perform deterministic operational reasoning often enough to make people dangerously optimistic.

This is precisely why deterministic models and governance matter.

Structured content, semantic markup, metadata governance, provenance tracking, DOM Graph RAG, iiRDS frameworks, knowledge graphs, RDF and OWL ontologies, context graphs, deterministic inference engines, orchestration platforms, Docs-as-Tests automation, and runtime observability together create something fundamentally different from prompt engineering. They create governed operational ecosystems capable of supporting trustworthy enterprise AI at scale."

https://medium.com/@nc_mike/deterministic-and-agentic-ai-architectures-for-technical-documentation-3fb2956a1334

#AI #GenerativeAI #DocsAsTests #LLMs #AgenticAI #DITAXML #AIAgents #TechnicalWriting #SoftwareDocumentation

"The future of enterprise technical documentation will not belong to organizations that merely generate more content with AI. It will belong to organizations that build semantically governed, operationally validated, and explainable knowledge ecosystems around AI generation.

Large language models are remarkable language-generation systems, but they remain fundamentally probabilistic, and no amount of vector-based probabilistic augmentation, recursive prompt gymnastics, or trillions of additional parameters magically transforms probabilistic token prediction into deterministic operational intelligence — regardless of what the AI snake-oil salesmen on LinkedIn insist between inspirational rocket-ship emojis. LLMs predict statistically likely outputs. They do not inherently understand operational correctness, governance policy, procedural safety, rollback integrity, regulatory compliance, or whether the “helpful” configuration change they just suggested is going to quietly detonate a production Kubernetes cluster at 2:13 a.m. while everyone is asleep and the on-call engineer is reconsidering their career choices.

That is not a moral failure of AI. It is simply the architectural reality of probabilistic systems pretending to perform deterministic operational reasoning often enough to make people dangerously optimistic.

This is precisely why deterministic models and governance matter.

Structured content, semantic markup, metadata governance, provenance tracking, DOM Graph RAG, iiRDS frameworks, knowledge graphs, RDF and OWL ontologies, context graphs, deterministic inference engines, orchestration platforms, Docs-as-Tests automation, and runtime observability together create something fundamentally different from prompt engineering. They create governed operational ecosystems capable of supporting trustworthy enterprise AI at scale."

https://medium.com/@nc_mike/deterministic-and-agentic-ai-architectures-for-technical-documentation-3fb2956a1334

#AI #GenerativeAI #DocsAsTests #LLMs #AgenticAI #DITAXML #AIAgents #TechnicalWriting #SoftwareDocumentation

"The future of enterprise technical documentation will not belong to organizations that merely generate more content with AI. It will belong to organizations that build semantically governed, operationally validated, and explainable knowledge ecosystems around AI generation.

Large language models are remarkable language-generation systems, but they remain fundamentally probabilistic, and no amount of vector-based probabilistic augmentation, recursive prompt gymnastics, or trillions of additional parameters magically transforms probabilistic token prediction into deterministic operational intelligence — regardless of what the AI snake-oil salesmen on LinkedIn insist between inspirational rocket-ship emojis. LLMs predict statistically likely outputs. They do not inherently understand operational correctness, governance policy, procedural safety, rollback integrity, regulatory compliance, or whether the “helpful” configuration change they just suggested is going to quietly detonate a production Kubernetes cluster at 2:13 a.m. while everyone is asleep and the on-call engineer is reconsidering their career choices.

That is not a moral failure of AI. It is simply the architectural reality of probabilistic systems pretending to perform deterministic operational reasoning often enough to make people dangerously optimistic.

This is precisely why deterministic models and governance matter.

Structured content, semantic markup, metadata governance, provenance tracking, DOM Graph RAG, iiRDS frameworks, knowledge graphs, RDF and OWL ontologies, context graphs, deterministic inference engines, orchestration platforms, Docs-as-Tests automation, and runtime observability together create something fundamentally different from prompt engineering. They create governed operational ecosystems capable of supporting trustworthy enterprise AI at scale."

https://medium.com/@nc_mike/deterministic-and-agentic-ai-architectures-for-technical-documentation-3fb2956a1334

#AI #GenerativeAI #DocsAsTests #LLMs #AgenticAI #DITAXML #AIAgents #TechnicalWriting #SoftwareDocumentation

"Back in 2024 I wrote that AI helps me remove boring work at the margins. This is fine for a lone writer, but how to scale this to an entire team of technical writers? How to make the system helpful but not intrusive? These are all questions I’m starting to answer now, partly through experimentation, but also through dialogue with practitioners and colleagues. One answer I’m testing these days relies on GitHub Agentic Workflows.

Following Four modes of AI-augmented technical writing, I thought of a way of distributing tooling effort across all modes through a tiered system where each level holds a different relationship with the writer. The result is four tiers: intake, local assistance, automated governance, and an MCP server that provides reliable knowledge to all. The idea is that AI assists the writer not just while writing, but also before and after they work on docs."

https://passo.uno/agentic-workflows-for-docs/

#TechnicalWriting #AI #GenerativeAI #LLMs #AIAgents #AgenticAI #AgenticWorkflows #SoftwareDocumentation #GitHub #DocsAsCode

"Back in 2024 I wrote that AI helps me remove boring work at the margins. This is fine for a lone writer, but how to scale this to an entire team of technical writers? How to make the system helpful but not intrusive? These are all questions I’m starting to answer now, partly through experimentation, but also through dialogue with practitioners and colleagues. One answer I’m testing these days relies on GitHub Agentic Workflows.

Following Four modes of AI-augmented technical writing, I thought of a way of distributing tooling effort across all modes through a tiered system where each level holds a different relationship with the writer. The result is four tiers: intake, local assistance, automated governance, and an MCP server that provides reliable knowledge to all. The idea is that AI assists the writer not just while writing, but also before and after they work on docs."

https://passo.uno/agentic-workflows-for-docs/

#TechnicalWriting #AI #GenerativeAI #LLMs #AIAgents #AgenticAI #AgenticWorkflows #SoftwareDocumentation #GitHub #DocsAsCode

"Back in 2024 I wrote that AI helps me remove boring work at the margins. This is fine for a lone writer, but how to scale this to an entire team of technical writers? How to make the system helpful but not intrusive? These are all questions I’m starting to answer now, partly through experimentation, but also through dialogue with practitioners and colleagues. One answer I’m testing these days relies on GitHub Agentic Workflows.

Following Four modes of AI-augmented technical writing, I thought of a way of distributing tooling effort across all modes through a tiered system where each level holds a different relationship with the writer. The result is four tiers: intake, local assistance, automated governance, and an MCP server that provides reliable knowledge to all. The idea is that AI assists the writer not just while writing, but also before and after they work on docs."

https://passo.uno/agentic-workflows-for-docs/

#TechnicalWriting #AI #GenerativeAI #LLMs #AIAgents #AgenticAI #AgenticWorkflows #SoftwareDocumentation #GitHub #DocsAsCode

"Back in 2024 I wrote that AI helps me remove boring work at the margins. This is fine for a lone writer, but how to scale this to an entire team of technical writers? How to make the system helpful but not intrusive? These are all questions I’m starting to answer now, partly through experimentation, but also through dialogue with practitioners and colleagues. One answer I’m testing these days relies on GitHub Agentic Workflows.

Following Four modes of AI-augmented technical writing, I thought of a way of distributing tooling effort across all modes through a tiered system where each level holds a different relationship with the writer. The result is four tiers: intake, local assistance, automated governance, and an MCP server that provides reliable knowledge to all. The idea is that AI assists the writer not just while writing, but also before and after they work on docs."

https://passo.uno/agentic-workflows-for-docs/

#TechnicalWriting #AI #GenerativeAI #LLMs #AIAgents #AgenticAI #AgenticWorkflows #SoftwareDocumentation #GitHub #DocsAsCode

"Back in 2024 I wrote that AI helps me remove boring work at the margins. This is fine for a lone writer, but how to scale this to an entire team of technical writers? How to make the system helpful but not intrusive? These are all questions I’m starting to answer now, partly through experimentation, but also through dialogue with practitioners and colleagues. One answer I’m testing these days relies on GitHub Agentic Workflows.

Following Four modes of AI-augmented technical writing, I thought of a way of distributing tooling effort across all modes through a tiered system where each level holds a different relationship with the writer. The result is four tiers: intake, local assistance, automated governance, and an MCP server that provides reliable knowledge to all. The idea is that AI assists the writer not just while writing, but also before and after they work on docs."

https://passo.uno/agentic-workflows-for-docs/

#TechnicalWriting #AI #GenerativeAI #LLMs #AIAgents #AgenticAI #AgenticWorkflows #SoftwareDocumentation #GitHub #DocsAsCode

I don't agree at all with the statement that "API documentation is not technical writing" and also with the notion a technical writer can't necessary know enough about programming without being a software developer - hint: how many Udemy courses are there about API development, API design and AP programming? Hundreds? Thousands?

Also, nowadays, with tools like Claude Code and Codex, testing APIs through platforms like Postman should be seen as stuff for QA analysts and not exactly for technical writers, since AI tools such as those allow you to have a more contextualized look at what a specific API endpoint does, specifically in terms of edge cases and "odd balls". As a technical writer, I can ask these tools to highlight specific use cases where the endpoint can be really useful.

That's not to say that the process can be completely automated. Not at all. Specially because an how-to guide explaining how to make use of an API endpoint couldn't essentially be completely triggered by an LLM. Besides, for the foreseeable future and probably even beyond that, the final output should always be reviewed by an engineer. In any case, what I'm talking about is totally different from automatically generally API reference documentation.

But there is no point in knowing how to send a request to an API endpoint and the typical response will be - both in case of success and error -, if I, as a developer, don't have a compelling enough reason to use that endpoint. Another totally different thing is an API Integration tutorial, that is, how to integrate a complete API into your own app. But here you will, of course, also need the intervention of a, guess what, TECHNICAL WRITER!! :-D

"I have said that API documentation is not technical writing and that it is a mistake to try. There are many details clients need to have. This includes format, presentation, and client experience."

https://robertdelwood.medium.com/more-about-api-documentation-errors-part-i-969999176c9f

#TechnicalWriting #API #APIs #APIDocumentation #SoftwareDocumentation

I don't agree at all with the statement that "API documentation is not technical writing" and also with the notion a technical writer can't necessary know enough about programming without being a software developer - hint: how many Udemy courses are there about API development, API design and AP programming? Hundreds? Thousands?

Also, nowadays, with tools like Claude Code and Codex, testing APIs through platforms like Postman should be seen as stuff for QA analysts and not exactly for technical writers, since AI tools such as those allow you to have a more contextualized look at what a specific API endpoint does, specifically in terms of edge cases and "odd balls". As a technical writer, I can ask these tools to highlight specific use cases where the endpoint can be really useful.

That's not to say that the process can be completely automated. Not at all. Specially because an how-to guide explaining how to make use of an API endpoint couldn't essentially be completely triggered by an LLM. Besides, for the foreseeable future and probably even beyond that, the final output should always be reviewed by an engineer. In any case, what I'm talking about is totally different from automatically generally API reference documentation.

But there is no point in knowing how to send a request to an API endpoint and the typical response will be - both in case of success and error -, if I, as a developer, don't have a compelling enough reason to use that endpoint. Another totally different thing is an API Integration tutorial, that is, how to integrate a complete API into your own app. But here you will, of course, also need the intervention of a, guess what, TECHNICAL WRITER!! :-D

"I have said that API documentation is not technical writing and that it is a mistake to try. There are many details clients need to have. This includes format, presentation, and client experience."

https://robertdelwood.medium.com/more-about-api-documentation-errors-part-i-969999176c9f

#TechnicalWriting #API #APIs #APIDocumentation #SoftwareDocumentation

I don't agree at all with the statement that "API documentation is not technical writing" and also with the notion a technical writer can't necessary know enough about programming without being a software developer - hint: how many Udemy courses are there about API development, API design and AP programming? Hundreds? Thousands?

Also, nowadays, with tools like Claude Code and Codex, testing APIs through platforms like Postman should be seen as stuff for QA analysts and not exactly for technical writers, since AI tools such as those allow you to have a more contextualized look at what a specific API endpoint does, specifically in terms of edge cases and "odd balls". As a technical writer, I can ask these tools to highlight specific use cases where the endpoint can be really useful.

That's not to say that the process can be completely automated. Not at all. Specially because an how-to guide explaining how to make use of an API endpoint couldn't essentially be completely triggered by an LLM. Besides, for the foreseeable future and probably even beyond that, the final output should always be reviewed by an engineer. In any case, what I'm talking about is totally different from automatically generally API reference documentation.

But there is no point in knowing how to send a request to an API endpoint and the typical response will be - both in case of success and error -, if I, as a developer, don't have a compelling enough reason to use that endpoint. Another totally different thing is an API Integration tutorial, that is, how to integrate a complete API into your own app. But here you will, of course, also need the intervention of a, guess what, TECHNICAL WRITER!! :-D

"I have said that API documentation is not technical writing and that it is a mistake to try. There are many details clients need to have. This includes format, presentation, and client experience."

https://robertdelwood.medium.com/more-about-api-documentation-errors-part-i-969999176c9f

#TechnicalWriting #API #APIs #APIDocumentation #SoftwareDocumentation

I don't agree at all with the statement that "API documentation is not technical writing" and also with the notion a technical writer can't necessary know enough about programming without being a software developer - hint: how many Udemy courses are there about API development, API design and AP programming? Hundreds? Thousands?

Also, nowadays, with tools like Claude Code and Codex, testing APIs through platforms like Postman should be seen as stuff for QA analysts and not exactly for technical writers, since AI tools such as those allow you to have a more contextualized look at what a specific API endpoint does, specifically in terms of edge cases and "odd balls". As a technical writer, I can ask these tools to highlight specific use cases where the endpoint can be really useful.

That's not to say that the process can be completely automated. Not at all. Specially because an how-to guide explaining how to make use of an API endpoint couldn't essentially be completely triggered by an LLM. Besides, for the foreseeable future and probably even beyond that, the final output should always be reviewed by an engineer. In any case, what I'm talking about is totally different from automatically generally API reference documentation.

But there is no point in knowing how to send a request to an API endpoint and the typical response will be - both in case of success and error -, if I, as a developer, don't have a compelling enough reason to use that endpoint. Another totally different thing is an API Integration tutorial, that is, how to integrate a complete API into your own app. But here you will, of course, also need the intervention of a, guess what, TECHNICAL WRITER!! :-D

"I have said that API documentation is not technical writing and that it is a mistake to try. There are many details clients need to have. This includes format, presentation, and client experience."

https://robertdelwood.medium.com/more-about-api-documentation-errors-part-i-969999176c9f

#TechnicalWriting #API #APIs #APIDocumentation #SoftwareDocumentation

I don't agree at all with the statement that "API documentation is not technical writing" and also with the notion a technical writer can't necessary know enough about programming without being a software developer - hint: how many Udemy courses are there about API development, API design and AP programming? Hundreds? Thousands?

Also, nowadays, with tools like Claude Code and Codex, testing APIs through platforms like Postman should be seen as stuff for QA analysts and not exactly for technical writers, since AI tools such as those allow you to have a more contextualized look at what a specific API endpoint does, specifically in terms of edge cases and "odd balls". As a technical writer, I can ask these tools to highlight specific use cases where the endpoint can be really useful.

That's not to say that the process can be completely automated. Not at all. Specially because an how-to guide explaining how to make use of an API endpoint couldn't essentially be completely triggered by an LLM. Besides, for the foreseeable future and probably even beyond that, the final output should always be reviewed by an engineer. In any case, what I'm talking about is totally different from automatically generally API reference documentation.

But there is no point in knowing how to send a request to an API endpoint and the typical response will be - both in case of success and error -, if I, as a developer, don't have a compelling enough reason to use that endpoint. Another totally different thing is an API Integration tutorial, that is, how to integrate a complete API into your own app. But here you will, of course, also need the intervention of a, guess what, TECHNICAL WRITER!! :-D

"I have said that API documentation is not technical writing and that it is a mistake to try. There are many details clients need to have. This includes format, presentation, and client experience."

https://robertdelwood.medium.com/more-about-api-documentation-errors-part-i-969999176c9f

#TechnicalWriting #API #APIs #APIDocumentation #SoftwareDocumentation

That seems a bit similar to what I currently do... ;-)

"To use an analogy about my process, compare the scenario to a senior tech writer (TW) working next to a junior TW, where the senior TW mostly provides observation and feedback (in this analogy, the junior TW represents the AI agent). The junior TW creates some docs and presents them to the senior TW, who leaves comments explaining what needs to change. The junior TW takes notes about all the feedback in a journal. By the end of the process, the junior TW has three pages of notes.

After the process finishes, those notes aren’t lost. They form the basis of the SKILL file. The next time the senior TW sits down with another junior TW (a different one, as the session changed), the new junior TW produces much better output thanks to the notes. With each iteration, the notes get more detailed — anticipating common errors, adding validation checks, laying a foundation so that each step doesn’t build from faulty information. After a dozen iterations, the senior TW finds they have less and less feedback to give.

Eventually, the senior TW no longer needs to sit next to the junior TW in close observation. The junior TW proceeds autonomously through each step in the SKILL and just shows the final result. One key difference from real mentorship, though: the AI agent doesn’t carry any memory between sessions. It reads the SKILL file cold each time. All the “learning” lives in the document, not in the agent. This makes the SKILL file itself the critical asset — if it’s vague or incomplete, the agent’s output regresses immediately."

https://idratherbewriting.com/blog/internal-skills-release-docs

#TechnicalWriting #APIs #APIDocumentation #Skills #AgenticAI #AI #GenerativeAI #LLMs #SoftwareDocumentation

That seems a bit similar to what I currently do... ;-)

"To use an analogy about my process, compare the scenario to a senior tech writer (TW) working next to a junior TW, where the senior TW mostly provides observation and feedback (in this analogy, the junior TW represents the AI agent). The junior TW creates some docs and presents them to the senior TW, who leaves comments explaining what needs to change. The junior TW takes notes about all the feedback in a journal. By the end of the process, the junior TW has three pages of notes.

After the process finishes, those notes aren’t lost. They form the basis of the SKILL file. The next time the senior TW sits down with another junior TW (a different one, as the session changed), the new junior TW produces much better output thanks to the notes. With each iteration, the notes get more detailed — anticipating common errors, adding validation checks, laying a foundation so that each step doesn’t build from faulty information. After a dozen iterations, the senior TW finds they have less and less feedback to give.

Eventually, the senior TW no longer needs to sit next to the junior TW in close observation. The junior TW proceeds autonomously through each step in the SKILL and just shows the final result. One key difference from real mentorship, though: the AI agent doesn’t carry any memory between sessions. It reads the SKILL file cold each time. All the “learning” lives in the document, not in the agent. This makes the SKILL file itself the critical asset — if it’s vague or incomplete, the agent’s output regresses immediately."

https://idratherbewriting.com/blog/internal-skills-release-docs

#TechnicalWriting #APIs #APIDocumentation #Skills #AgenticAI #AI #GenerativeAI #LLMs #SoftwareDocumentation

That seems a bit similar to what I currently do... ;-)

"To use an analogy about my process, compare the scenario to a senior tech writer (TW) working next to a junior TW, where the senior TW mostly provides observation and feedback (in this analogy, the junior TW represents the AI agent). The junior TW creates some docs and presents them to the senior TW, who leaves comments explaining what needs to change. The junior TW takes notes about all the feedback in a journal. By the end of the process, the junior TW has three pages of notes.

After the process finishes, those notes aren’t lost. They form the basis of the SKILL file. The next time the senior TW sits down with another junior TW (a different one, as the session changed), the new junior TW produces much better output thanks to the notes. With each iteration, the notes get more detailed — anticipating common errors, adding validation checks, laying a foundation so that each step doesn’t build from faulty information. After a dozen iterations, the senior TW finds they have less and less feedback to give.

Eventually, the senior TW no longer needs to sit next to the junior TW in close observation. The junior TW proceeds autonomously through each step in the SKILL and just shows the final result. One key difference from real mentorship, though: the AI agent doesn’t carry any memory between sessions. It reads the SKILL file cold each time. All the “learning” lives in the document, not in the agent. This makes the SKILL file itself the critical asset — if it’s vague or incomplete, the agent’s output regresses immediately."

https://idratherbewriting.com/blog/internal-skills-release-docs

#TechnicalWriting #APIs #APIDocumentation #Skills #AgenticAI #AI #GenerativeAI #LLMs #SoftwareDocumentation

That seems a bit similar to what I currently do... ;-)

"To use an analogy about my process, compare the scenario to a senior tech writer (TW) working next to a junior TW, where the senior TW mostly provides observation and feedback (in this analogy, the junior TW represents the AI agent). The junior TW creates some docs and presents them to the senior TW, who leaves comments explaining what needs to change. The junior TW takes notes about all the feedback in a journal. By the end of the process, the junior TW has three pages of notes.

After the process finishes, those notes aren’t lost. They form the basis of the SKILL file. The next time the senior TW sits down with another junior TW (a different one, as the session changed), the new junior TW produces much better output thanks to the notes. With each iteration, the notes get more detailed — anticipating common errors, adding validation checks, laying a foundation so that each step doesn’t build from faulty information. After a dozen iterations, the senior TW finds they have less and less feedback to give.

Eventually, the senior TW no longer needs to sit next to the junior TW in close observation. The junior TW proceeds autonomously through each step in the SKILL and just shows the final result. One key difference from real mentorship, though: the AI agent doesn’t carry any memory between sessions. It reads the SKILL file cold each time. All the “learning” lives in the document, not in the agent. This makes the SKILL file itself the critical asset — if it’s vague or incomplete, the agent’s output regresses immediately."

https://idratherbewriting.com/blog/internal-skills-release-docs

#TechnicalWriting #APIs #APIDocumentation #Skills #AgenticAI #AI #GenerativeAI #LLMs #SoftwareDocumentation

That seems a bit similar to what I currently do... ;-)

"To use an analogy about my process, compare the scenario to a senior tech writer (TW) working next to a junior TW, where the senior TW mostly provides observation and feedback (in this analogy, the junior TW represents the AI agent). The junior TW creates some docs and presents them to the senior TW, who leaves comments explaining what needs to change. The junior TW takes notes about all the feedback in a journal. By the end of the process, the junior TW has three pages of notes.

After the process finishes, those notes aren’t lost. They form the basis of the SKILL file. The next time the senior TW sits down with another junior TW (a different one, as the session changed), the new junior TW produces much better output thanks to the notes. With each iteration, the notes get more detailed — anticipating common errors, adding validation checks, laying a foundation so that each step doesn’t build from faulty information. After a dozen iterations, the senior TW finds they have less and less feedback to give.

Eventually, the senior TW no longer needs to sit next to the junior TW in close observation. The junior TW proceeds autonomously through each step in the SKILL and just shows the final result. One key difference from real mentorship, though: the AI agent doesn’t carry any memory between sessions. It reads the SKILL file cold each time. All the “learning” lives in the document, not in the agent. This makes the SKILL file itself the critical asset — if it’s vague or incomplete, the agent’s output regresses immediately."

https://idratherbewriting.com/blog/internal-skills-release-docs

#TechnicalWriting #APIs #APIDocumentation #Skills #AgenticAI #AI #GenerativeAI #LLMs #SoftwareDocumentation

"Docs are beautiful when conceptual docs let the reader see the architecture, or when a tutorial lets them see their own hands on the keyboard. Diagrams and screenshots help, but they often compensate for prose that failed to produce an image on its own. Docs are visible when the reader can close their eyes and still see what the page described."

https://passo.uno/what-makes-docs-beautiful/

#Documentation #DocsAsProduct #SoftwareDocumentation #TechnicalWriting #SoftwareDevelopment

"Docs are beautiful when conceptual docs let the reader see the architecture, or when a tutorial lets them see their own hands on the keyboard. Diagrams and screenshots help, but they often compensate for prose that failed to produce an image on its own. Docs are visible when the reader can close their eyes and still see what the page described."

https://passo.uno/what-makes-docs-beautiful/

#Documentation #DocsAsProduct #SoftwareDocumentation #TechnicalWriting #SoftwareDevelopment

"Docs are beautiful when conceptual docs let the reader see the architecture, or when a tutorial lets them see their own hands on the keyboard. Diagrams and screenshots help, but they often compensate for prose that failed to produce an image on its own. Docs are visible when the reader can close their eyes and still see what the page described."

https://passo.uno/what-makes-docs-beautiful/

#Documentation #DocsAsProduct #SoftwareDocumentation #TechnicalWriting #SoftwareDevelopment

"Docs are beautiful when conceptual docs let the reader see the architecture, or when a tutorial lets them see their own hands on the keyboard. Diagrams and screenshots help, but they often compensate for prose that failed to produce an image on its own. Docs are visible when the reader can close their eyes and still see what the page described."

https://passo.uno/what-makes-docs-beautiful/

#Documentation #DocsAsProduct #SoftwareDocumentation #TechnicalWriting #SoftwareDevelopment

"Docs are beautiful when conceptual docs let the reader see the architecture, or when a tutorial lets them see their own hands on the keyboard. Diagrams and screenshots help, but they often compensate for prose that failed to produce an image on its own. Docs are visible when the reader can close their eyes and still see what the page described."

https://passo.uno/what-makes-docs-beautiful/

#Documentation #DocsAsProduct #SoftwareDocumentation #TechnicalWriting #SoftwareDevelopment

The great thing that Claude Code - or OpenAI Codex - brings to technical writers is that they can assess the accuracy of any piece of documentation that relies on software code, by analyzing the relevant code base(s).

This is extremely helpful because it definitely helps you fact-check your docs against the source code, namely to see if the Subject Matter Experts (SMEs) were bullshitting you or if the docs became outdated due to the cadence of new releases.

Another advantage is that even if you work in an organization with its own QA team, you can help them catch bugs at an earlier stage of the Software Development Life Cycle (SDLC). For example, yesterday I found an inconsistency between how a certain behavior was coded in the backend and how that same behavior was interpreted by the frontend.

And the best thing is that, since Claude Code does not entirely relies on neural networks but rather also uses regular expressions, the results have an higher degree of determinancy than the ones offered by common LLMs. Even though it's not perfect and you always have to tell it where to direct its attention (meaning the name of the most relevant repository) , this ability of taking advantage of a "Ai-based sniffer" for code is terrific.

For all these reasons I believe that every technical writer that doesn't use Claude Code or a similar toll in its own regular workflows will be immensely disadvantaged.

#TechnicalWriting #AI #GenerativeAI #SoftwareDocumentation #Claude #LLMs #GenerativeAI #ClaudeCode #SoftwareDevelopment #QA

The great thing that Claude Code - or OpenAI Codex - brings to technical writers is that they can assess the accuracy of any piece of documentation that relies on software code, by analyzing the relevant code base(s).

This is extremely helpful because it definitely helps you fact-check your docs against the source code, namely to see if the Subject Matter Experts (SMEs) were bullshitting you or if the docs became outdated due to the cadence of new releases.

Another advantage is that even if you work in an organization with its own QA team, you can help them catch bugs at an earlier stage of the Software Development Life Cycle (SDLC). For example, yesterday I found an inconsistency between how a certain behavior was coded in the backend and how that same behavior was interpreted by the frontend.

And the best thing is that, since Claude Code does not entirely relies on neural networks but rather also uses regular expressions, the results have an higher degree of determinancy than the ones offered by common LLMs. Even though it's not perfect and you always have to tell it where to direct its attention (meaning the name of the most relevant repository) , this ability of taking advantage of a "Ai-based sniffer" for code is terrific.

For all these reasons I believe that every technical writer that doesn't use Claude Code or a similar toll in its own regular workflows will be immensely disadvantaged.

#TechnicalWriting #AI #GenerativeAI #SoftwareDocumentation #Claude #LLMs #GenerativeAI #ClaudeCode #SoftwareDevelopment #QA

The great thing that Claude Code - or OpenAI Codex - brings to technical writers is that they can assess the accuracy of any piece of documentation that relies on software code, by analyzing the relevant code base(s).

This is extremely helpful because it definitely helps you fact-check your docs against the source code, namely to see if the Subject Matter Experts (SMEs) were bullshitting you or if the docs became outdated due to the cadence of new releases.

Another advantage is that even if you work in an organization with its own QA team, you can help them catch bugs at an earlier stage of the Software Development Life Cycle (SDLC). For example, yesterday I found an inconsistency between how a certain behavior was coded in the backend and how that same behavior was interpreted by the frontend.

And the best thing is that, since Claude Code does not entirely relies on neural networks but rather also uses regular expressions, the results have an higher degree of determinancy than the ones offered by common LLMs. Even though it's not perfect and you always have to tell it where to direct its attention (meaning the name of the most relevant repository) , this ability of taking advantage of a "Ai-based sniffer" for code is terrific.

For all these reasons I believe that every technical writer that doesn't use Claude Code or a similar toll in its own regular workflows will be immensely disadvantaged.

#TechnicalWriting #AI #GenerativeAI #SoftwareDocumentation #Claude #LLMs #GenerativeAI #ClaudeCode #SoftwareDevelopment #QA

The great thing that Claude Code - or OpenAI Codex - brings to technical writers is that they can assess the accuracy of any piece of documentation that relies on software code, by analyzing the relevant code base(s).

This is extremely helpful because it definitely helps you fact-check your docs against the source code, namely to see if the Subject Matter Experts (SMEs) were bullshitting you or if the docs became outdated due to the cadence of new releases.

Another advantage is that even if you work in an organization with its own QA team, you can help them catch bugs at an earlier stage of the Software Development Life Cycle (SDLC). For example, yesterday I found an inconsistency between how a certain behavior was coded in the backend and how that same behavior was interpreted by the frontend.

And the best thing is that, since Claude Code does not entirely relies on neural networks but rather also uses regular expressions, the results have an higher degree of determinancy than the ones offered by common LLMs. Even though it's not perfect and you always have to tell it where to direct its attention (meaning the name of the most relevant repository) , this ability of taking advantage of a "Ai-based sniffer" for code is terrific.

For all these reasons I believe that every technical writer that doesn't use Claude Code or a similar toll in its own regular workflows will be immensely disadvantaged.

#TechnicalWriting #AI #GenerativeAI #SoftwareDocumentation #Claude #LLMs #GenerativeAI #ClaudeCode #SoftwareDevelopment #QA

The great thing that Claude Code - or OpenAI Codex - brings to technical writers is that they can assess the accuracy of any piece of documentation that relies on software code, by analyzing the relevant code base(s).

This is extremely helpful because it definitely helps you fact-check your docs against the source code, namely to see if the Subject Matter Experts (SMEs) were bullshitting you or if the docs became outdated due to the cadence of new releases.

Another advantage is that even if you work in an organization with its own QA team, you can help them catch bugs at an earlier stage of the Software Development Life Cycle (SDLC). For example, yesterday I found an inconsistency between how a certain behavior was coded in the backend and how that same behavior was interpreted by the frontend.

And the best thing is that, since Claude Code does not entirely relies on neural networks but rather also uses regular expressions, the results have an higher degree of determinancy than the ones offered by common LLMs. Even though it's not perfect and you always have to tell it where to direct its attention (meaning the name of the most relevant repository) , this ability of taking advantage of a "Ai-based sniffer" for code is terrific.

For all these reasons I believe that every technical writer that doesn't use Claude Code or a similar toll in its own regular workflows will be immensely disadvantaged.

#TechnicalWriting #AI #GenerativeAI #SoftwareDocumentation #Claude #LLMs #GenerativeAI #ClaudeCode #SoftwareDevelopment #QA

This article is great in the sense that it describes most of what I'm doing nowadays as a technical writer. I even put different LLMs reviewing each other's drafts, which is a lot of fun. That's why, personally, I can't be so pessimistic as others are currently being. LLMs are just a new technology that you need to incorporate in your workflows. Of course, there are some skills that will probably become atrofied. At the same time, a new set of skills is emerging. If you don't see that. you will be completely left behind. You just need to use these tools by making use of critical thinking.

"After deliberation for a few months, I reached a conclusion about what I wanted to say: the model that’s emerging is a cyborg model of technical writing, a humans + AI combination. This is in contrast to the many articles, which now seem to come at an even faster pace, saying that AI will replace human labor. I realize there’s a lot of opinion on this debate, but my argument for why the humans + AI (cyborgs) model is the winning one, rather than replacement, is because of this observation: almost no tech writers at my work have automated complex processes using AI. And in my own use of AI over the past few years, the model that’s emerged is a close intertwining of machine and human interaction to produce content. I’m talking with AI all day. It’s not doing much on its own without my constant steering, direction, and feedback."

https://idratherbewriting.com/blog/cyborg-model-emerging-talk

#AI #GenerativeAI #LLMs #Chatbots #TechnicalWriting #TechnicalDocumentation #SoftwareDevelopment #SoftwareDocumentation

This article is great in the sense that it describes most of what I'm doing nowadays as a technical writer. I even put different LLMs reviewing each other's drafts, which is a lot of fun. That's why, personally, I can't be so pessimistic as others are currently being. LLMs are just a new technology that you need to incorporate in your workflows. Of course, there are some skills that will probably become atrofied. At the same time, a new set of skills is emerging. If you don't see that. you will be completely left behind. You just need to use these tools by making use of critical thinking.

"After deliberation for a few months, I reached a conclusion about what I wanted to say: the model that’s emerging is a cyborg model of technical writing, a humans + AI combination. This is in contrast to the many articles, which now seem to come at an even faster pace, saying that AI will replace human labor. I realize there’s a lot of opinion on this debate, but my argument for why the humans + AI (cyborgs) model is the winning one, rather than replacement, is because of this observation: almost no tech writers at my work have automated complex processes using AI. And in my own use of AI over the past few years, the model that’s emerged is a close intertwining of machine and human interaction to produce content. I’m talking with AI all day. It’s not doing much on its own without my constant steering, direction, and feedback."

https://idratherbewriting.com/blog/cyborg-model-emerging-talk

#AI #GenerativeAI #LLMs #Chatbots #TechnicalWriting #TechnicalDocumentation #SoftwareDevelopment #SoftwareDocumentation

This article is great in the sense that it describes most of what I'm doing nowadays as a technical writer. I even put different LLMs reviewing each other's drafts, which is a lot of fun. That's why, personally, I can't be so pessimistic as others are currently being. LLMs are just a new technology that you need to incorporate in your workflows. Of course, there are some skills that will probably become atrofied. At the same time, a new set of skills is emerging. If you don't see that. you will be completely left behind. You just need to use these tools by making use of critical thinking.

"After deliberation for a few months, I reached a conclusion about what I wanted to say: the model that’s emerging is a cyborg model of technical writing, a humans + AI combination. This is in contrast to the many articles, which now seem to come at an even faster pace, saying that AI will replace human labor. I realize there’s a lot of opinion on this debate, but my argument for why the humans + AI (cyborgs) model is the winning one, rather than replacement, is because of this observation: almost no tech writers at my work have automated complex processes using AI. And in my own use of AI over the past few years, the model that’s emerged is a close intertwining of machine and human interaction to produce content. I’m talking with AI all day. It’s not doing much on its own without my constant steering, direction, and feedback."

https://idratherbewriting.com/blog/cyborg-model-emerging-talk

#AI #GenerativeAI #LLMs #Chatbots #TechnicalWriting #TechnicalDocumentation #SoftwareDevelopment #SoftwareDocumentation

This article is great in the sense that it describes most of what I'm doing nowadays as a technical writer. I even put different LLMs reviewing each other's drafts, which is a lot of fun. That's why, personally, I can't be so pessimistic as others are currently being. LLMs are just a new technology that you need to incorporate in your workflows. Of course, there are some skills that will probably become atrofied. At the same time, a new set of skills is emerging. If you don't see that. you will be completely left behind. You just need to use these tools by making use of critical thinking.

"After deliberation for a few months, I reached a conclusion about what I wanted to say: the model that’s emerging is a cyborg model of technical writing, a humans + AI combination. This is in contrast to the many articles, which now seem to come at an even faster pace, saying that AI will replace human labor. I realize there’s a lot of opinion on this debate, but my argument for why the humans + AI (cyborgs) model is the winning one, rather than replacement, is because of this observation: almost no tech writers at my work have automated complex processes using AI. And in my own use of AI over the past few years, the model that’s emerged is a close intertwining of machine and human interaction to produce content. I’m talking with AI all day. It’s not doing much on its own without my constant steering, direction, and feedback."

https://idratherbewriting.com/blog/cyborg-model-emerging-talk

#AI #GenerativeAI #LLMs #Chatbots #TechnicalWriting #TechnicalDocumentation #SoftwareDevelopment #SoftwareDocumentation

"With AI, the writer’s role moves to what I call context ownership. This is not a soft concept. A context owner is the person in your organization who governs what your AI tools know, how your content is structured, whether the output meets your quality and accuracy standards, and how your documentation systems connect to your product and engineering workflows.

In practice, context ownership looks like this:

A context owner defines and maintains the templates, standards, and structural rules that AI tools follow. Without these, AI produces content that is internally consistent within a single document but inconsistent across your documentation as a whole. Your customers notice, even if you don’t.

A context owner reviews and validates AI-generated drafts against product reality. AI tools do not know what your product actually does in edge cases. They do not know what changed in the last release that hasn’t been documented yet. They do not know that the API endpoint described in the engineering spec was modified during implementation. The context owner does.

A context owner manages the documentation pipeline. In a modern documentation operation, this means version control, docs-as-code workflows, API-driven publishing, and automated quality checks. These are technical systems that require technical management. AI can operate within these systems, but it cannot design, maintain, or troubleshoot them.

A context owner bridges engineering and customer-facing content. This is the function that has never been automated in any transition, and AI has not changed that. Someone has to understand what engineering built, determine what customers need to know about it, and make sure the documentation connects those two realities accurately.
(...)
This is not a diminished version of the writer’s role. It is a more senior, more technical role than “writer” has traditionally implied"
https://greenmtndocs.com/2026-03-25-ive-seen-this-before/
#AI #LLMs #TechnicalWriting #SoftwareDocumentation #ContextEngineering

"To begin with, everything you document has to be in a format that's as structured and machine-readable as possible. The key here is to disambiguate as much as you can, even if you have to repeat yourself. So, don't bother with the formatting of your documentation or the look and feel of your API portal. Instead, focus on using well-known API definition standards based on machine-readable formats. Use OpenAPI for documenting REST APIs, AsyncAPI for asynchronous APIs, Protocol Buffers for gRPC, and the GraphQL Schema Definition Language. Whenever possible, store the API definitions in several formats, such as JSON and YAML, for easy interpretation by AI agents.

But that's not enough. If you don't have all your operations clearly defined, AI agents will have a hard time understanding what they can do. Make sure you clearly define all operation parameters. Specify what the input types are so there are no misunderstandings. So, instead of saying that everything is a "string," identify each individual input format."

https://apichangelog.substack.com/p/api-documentation-for-machines

#APIs #APIDocumentation #AI #AIAgents #LLMs #OpenAPI #TechnicalWriting #SoftwareDocumentation #Programming

The problem is that most companies with the resources to properly implement role fluidity only want to hire "unicorns." Having worked in hybrid roles at smaller companies before and after the widespread adoption of LLMs, I must say that it's a recipe for burnout. This is not only because it's difficult to assess the quality of your work, but also because, in practice, companies don't care much about documentation. In reality, you'd mostly be a software developer doing some documentation in your "free time."

Another problem with this model of a fluid software documentation team is that it assumes there are or will be software companies willing to prioritize documentation as a sector that deserves its own department. However, technical writers are often placed under the product umbrella, which isn't necessarily bad. In fact, it's much better than being placed under "marketing." Unfortunately, if role fluidity ever becomes the norm, I'm afraid it will most likely start with engineering.

https://passo.uno/docs-team-of-the-future/

#TechnicalWriting #SoftwareDocumentation #Programming #SoftwareDevelopment #AI #LLMs

For the forseeable future, AI tools will continue to generate such incomplete and sometime hallucinated outputs that there will be a continuing need for a "human-in-the-loop" to not only use several LLMs to review each other's output but to fact-check the final output. Using one LLM alone results in mediocre quality. Using two LLMs results in (sometimes very) good quality. Use three LLMs with human verification for great/outstanding results.

"1,131 people across the documentation industry responded to the 2026 State of Docs survey — more than 2.5x the number of respondents last year. But the size of the sample matters less than what it represents: a genuine cross-section of the people who create, manage, evaluate, and depend on documentation.

Documentation’s role in purchase decisions is stable and strong, and the case that docs drive business value is well established. The shift this year is in what documentation is being asked to do, and who — and what — is consuming it.

AI has crossed the mainstream threshold for documentation, both in how docs get written and how they get consumed. Users are arriving through AI-powered search tools, coding assistants, and MCP servers. Documentation is becoming the data layer that feeds AI products, onboarding wizards, and developer tools. The teams investing in this shift are treating documentation as context infrastructure, not just a collection of pages.

But adoption has outrun governance, and the gap matters. Most teams are using AI without guidelines in place, and documentation carries a higher accuracy bar than most content. After all, one wrong instruction can break a user’s implementation and erode trust in the product.
(...)
Writers are spending less time drafting and more time fact-checking, validating, and building the context systems that make AI output worth refining."

https://www.stateofdocs.com/2026/introduction-and-demographics

#TechnicalWriting #TechnicalCommunication #SoftwareDocumentation #DocsAsProduct #AI #GenerativeAI

"Start small:

Pick one repeatable task that an agent currently handles without explicit guidance. Document it as a skill with entry criteria, steps, and exit criteria.

Validate it. Install skill-validator and run skill-validator check against your skill. Fix what it finds.

Test it with the agent. Invoke the skill explicitly and observe whether the agent follows it as written. Where it deviates, the skill is probably ambiguous.

Add validation to CI. Once you have a few skills, the CI integration keeps them from degrading as the project evolves.

Perhaps unsurprisingly, this is the same pattern I described for project descriptions: start with one file, observe how agents respond, iterate. The difference is that skills demand more precision because they're more prescriptive. That higher quality bar makes deterministic validation tooling valuable; you get feedback on skill quality before the agent runs, not after."

https://instructionmanuel.com/writing-skills-agents-can-execute

#AI #AIAgents #GenerativeAI #Skills #LLMs #TechnicalWriting #Documentation #SoftwareDocumentation

"In my post The Emerging Picture of a Changed Profession: Cyborg Technical Writers — Augmented, Not Replaced, by AI, I mentioned an upcoming presentation I'm giving to students and faculty. I argue that the future of the profession is the cyborg model, where machines augment our capabilities rather than replace us. In this post, I share notes about what skills a tech writer would need to learn to thrive in this world of augmentation.

If you have feedback about these skills, let me know. My intent here is to demonstrate what actual skills should be emphasized for those entering the profession, or for those currently in the profession who want to get ahead with AI. Note that the following sections are mostly bullet points, in the form of notes."

https://idratherbewriting.com/blog/10-principles-of-cyborg-technical-writer

#TechnicalWriting #TechnicalCommunication #SoftwareDocumentation #Documentation #AI #GenerativeAI #LLMs

"I ask AI to explain things all the time. If I observe it do something that I want to learn more about, I ask it. I look at its outputs and ask it to explain decisions it made or how it implemented something. I ask it to help me brainstorm about things, help me think through edge cases or performance considerations, you name it. If the thing that it is explaining has some implication I need to verify, I ask it to find me a link that backs up what it is saying. And then I look at the link to make sure the content is real, comes from a reasonable source, and actually backs up what the AI says. And probaly also ask it questions about the surface area around the thing, until I’m sure I understand it.

If you approach the AI upskill process as a collaborative learning process, where you can interrogate the tool you’re learning about its capabilities, how and why it’s chosing to do the things it’s doing, and to explain anything you don’t understand along the way - you’re unlocking a super power.

AND you have the comfort of knowing you’re asking all your questions of a talking box that won’t remember what you asked the next time it chats with you. So even if you do think it’s judging you, it has amnesia and that judgement won’t last beyond closing the session!"

https://dacharycarey.com/2026/02/23/upskilling-in-ai-age/

#TechnicalWriting #AI #LLMs #AIAgents #Chatbots #SoftwareDocumentation

Indeed, we can't allow autopilot to head into a whirlwind...

"We may be doing docs-as-code, but docs are not code. Docs run on people, and people are a messy tangle of goals, skills, and emotions. When docs hit the brain, they meet varying expectations, knowledge levels, reading abilities, and needs. None of this can be reproduced or simplified to a single pattern, but good docs use structure and words wisely to produce the best possible linguistic shape that can land safely on most people’s heads. Only humans can decide whether that message is getting across in the right way.

Getting there is a balancing act between business needs, user needs, and your own. That’s the diplomatic tension that forces all good tech writers to slow down and consider all points of view in the room as if they were in the middle of a spaghetti Western standoff. Slowing down is a deliberate, necessary act in all crafts, and tech writing is no exception. No matter how fast LLMs can churn out drafts, they don’t understand the tension in tech writing, to which we’re adding AI itself as an additional consumer of docs.
(...)
The quality of the docs I produce is still high, I was saying. That’s because I’m not letting LLMs take the steering wheel, and because I’m building new habits around them: setting up guardrails, automating what can be automated, and keeping my hands on the decisions that matter. I can do that because I know what good docs look like, and because I’ve been doing this long enough to feel when something’s off. That intuition came from years of wrestling with products and watching users struggle with pages I thought were clear. AI can help me write faster. It cannot replace the slow accumulation of judgment that tells me when to stop."

https://passo.uno/real-cost-of-documentation/

#TechnicalWriting #SoftwareDocumentation #AI #DocsAsCode #GenerativeAI #LLMs #SoftwareDevelopment #AISlop #Programming #TechnicalCommunication #Documentation

"Too often, API documentation writing is introduced as a series of rules or gut feeling about what seems obvious. Beginning writing, that’s a good approach. They’re easily understood and conform to. They’re rarely wrong. They’re far from complete, however.

API documentation writing is an art, not a science. As the artist, your influence is no less important than anyone else’s. But you’ll need to understand more in order to take the writing to a new level. You’ll need to know theory, the hows and whys, and to think like a programmer. The theory here is not only to connect with clients but also to present information in the most efficient way possible. It’s the last points that learning API documentation writing does not do well.

The following is a talk through. I talk about an element in conversational detail. I aim to discuss the important points, why an approach may be inappropriate, what the goals should be, and how to fix it. Along the way, I may make blunt statements. I do that for effect. By exposing the reason for the critique, we can get an understanding of the solution. We’ll look at this from the writer’s perspective."

https://robertdelwood.medium.com/improving-api-documentation-describing-one-parameter-at-a-time-cb53a89637a2?postPublishedType=initial

#TechnicalWriting #APIDocumentation #SoftwareDocumentation #SoftwareDevelopment #Programming #APIs #TechnicalCommunication

"Test your documentation site against the Agent-Friendly Documentation Spec.

Agents don't use docs like humans. They hit truncation limits, get walls of CSS instead of content, can't follow cross-host redirects, and don't know about quality-of-life improvements like llms.txt or .md docs pages that would make life swell. Maybe this is because the industry has lacked guidance - until now.

afdocs runs 21 checks across 8 categories to evaluate how well your docs serve agent consumers. 10 are fully implemented; the rest return skip until completed."

https://www.npmjs.com/package/afdocs

#TechnicalWriting #SoftwareDocumentation #AI #AIAgents #Afdocs #Markdown #DocsAsCode #LLMSTXT

"Test your documentation site against the Agent-Friendly Documentation Spec.

Agents don't use docs like humans. They hit truncation limits, get walls of CSS instead of content, can't follow cross-host redirects, and don't know about quality-of-life improvements like llms.txt or .md docs pages that would make life swell. Maybe this is because the industry has lacked guidance - until now.

afdocs runs 21 checks across 8 categories to evaluate how well your docs serve agent consumers. 10 are fully implemented; the rest return skip until completed."

https://www.npmjs.com/package/afdocs

#TechnicalWriting #SoftwareDocumentation #AI #AIAgents #Afdocs #Markdown #DocsAsCode #LLMSTXT