We need to write a concise 400-500 word Dev.to article. Must be between 400-500 words inclusive. We must count words. Provide title, subheadings, paragraphs. Must not reveal exact prompts or detailed configurations. Must include one specific tool name and its purpose (from facts). The facts mention: Use a free CI service like GitHub Actions for your docs repo. So we can mention GitHub Actions as the tool. Also mention maybe OpenAI or Claude as AI service. But need one specific tool name and its purpose. We'll choose GitHub Actions.

We need to follow structure: Title, intro (2-3 sentences), Core: explain ONE key principle or framework clearly, Mini-scenario: 2 sentences showing principle in action, Implementation: 3 high-level steps (not exact prompts), Conclusion: summarize key takeaways only (no e-book promotion, URLs, discount codes). Must include subheadings with markdown.

We must not reveal exact prompts or detailed configurations. So we can talk about feeding diff output into an AI agent with a clear prompt but not give the exact prompt.

We need to count words. Let's draft around 440 words.

We'll write:

Introduction

[2-3 sentences]

Core Principle: Continuous Sync via Event‑Driven AI Agents

[Explain one key principle]

Mini‑Scenario

[2 sentences]

Implementation: Three High‑Level Steps

[Step 1, Step 2, Step 3]

Conclusion

[Summarize]

Now count words.

Let's draft and then count.

Draft:

Taming Version Drift: Automating Documentation Updates from API Changes

Introduction

Keeping API docs in sync with code is a constant headache for freelance technical writers. Every new release risks outdated snippets and missing parameter notes, eroding trust with developers. Automating the detection and update loop turns this reactive chore into a seamless, reliable process.

Core Principle: Continuous Sync via Event‑Driven AI Agents

The key idea is to treat documentation as a downstream consumer of API changes, triggered automatically whenever a new version is published. By hooking into repository events (like a push tag), a workflow fetches the diff, feeds it to an AI summarizer, and creates a ready‑to‑act issue in the docs repo. This closes the loop between code and prose without manual tracking, ensuring that every breaking change, deprecation, or new endpoint surfaces as a concrete task with context.

Mini‑Scenario

Imagine a freelance writer maintains the docs for a SaaS API. When the team pushes a v2.1 tag, the workflow spots a removed limit parameter, asks the AI to draft a deprecation note, and opens an issue pointing to authentication.md and quickstart.md. The writer then edits those files, confident nothing was missed.

Implementation: Three High‑Level Steps

1. Set up event detection – Configure GitHub Actions in your docs repository to listen for release events from the API repo (using the repository dispatch or webhook). The action pulls the latest release notes and generates a file‑level diff.
2. Invoke AI summarization – Pass the diff to a language model (e.g., OpenAI or Claude) with a prompt that asks for a concise change list, impacted files, and suggested wording updates. The model returns a markdown‑formatted summary.
3. Create and enrich the issue – Use the GitHub API within the same workflow to open an issue titled with the change date, post the AI summary as the body, and add labels or assignees that route it to the appropriate writer or maintainer.

Conclusion

By turning API version releases into automated triggers, you eliminate manual drift checks and gain instant, AI‑enhanced visibility into what needs updating. The result is fresher documentation, fewer missed changes, and more time to focus on crafting clear, useful content for developers.

Now count words.

Let's count manually.

Title line: "Taming Version Drift: Automating Documentation Updates from API Changes" – words: Taming(1) Version2 Drift:3 Automating4 Documentation5 Updates6 from7 API8 Changes9

But we need to count all words in article. Let's count everything.

I'll copy text and count.

# Taming Version Drift: Automating Documentation Updates from API Changes

## Introduction
Keeping API docs in sync with code is a constant headache for freelance technical writers. Every new release risks outdated snippets and missing parameter notes, eroding trust with developers. Automating the detection and update loop turns this reactive chore into a seamless, reliable process.

## Core Principle: Continuous Sync via Event‑Driven AI Agents
The key idea is to treat documentation as a downstream consumer of API changes, triggered automatically whenever a new version is published. By hooking into repository events (like a push tag), a workflow fetches the diff, feeds it to an AI summarizer, and creates a ready‑to‑act issue in the docs repo. This closes the loop between code and prose without manual tracking, ensuring that every breaking change, deprecation, or new endpoint surfaces as a concrete task with context.

## Mini‑Scenario
Imagine a freelance writer maintains the docs for a SaaS API. When the team pushes a v2.1 tag, the workflow spots a removed `limit` parameter, asks the AI to draft a deprecation note, and opens an issue pointing to `authentication.md` and `quickstart.md`. The writer then edits those files, confident nothing was missed.

## Implementation: Three High‑Level Steps
1. **Set up event detection** – Configure GitHub Actions in your docs repository to listen for release events from the API repo (using the repository dispatch or webhook). The action pulls the latest release notes and generates a file‑level diff.
2. **Invoke AI summarization** – Pass the diff to a language model (e.g., OpenAI or Claude) with a prompt that asks for a concise change list, impacted files, and suggested wording updates. The model returns a markdown‑formatted summary.
3. **Create and enrich the issue** – Use the GitHub API within the same workflow to open an issue titled with the change date, post the AI summary as the body, and add labels or assignees that route it to the appropriate writer or maintainer.

## Conclusion
By turning API version releases into automated triggers, you eliminate manual drift checks and gain instant, AI‑enhanced visibility into what needs updating. The result is fresher documentation, fewer missed changes, and more time to focus on crafting clear, useful content for developers.

Now count words.

I'll count line by line.

First line: "# Taming Version Drift: Automating Documentation Updates from API Changes"
Words ignoring #: Taming(1) Version2 Drift:3 Automating4 Documentation5 Updates6 from7 API8 Changes9 => 9 words.

Blank line not counted.

Introduction

"## Introduction" => Introduction1? Actually "Introduction" counts as a word. So we count as part of heading. Let's count words in heading as well? Usually word count includes everything. We'll count heading words as well.

"## Introduction" => Introduction (1) => 1 word.

Now paragraph lines:

"Keeping API docs in sync with code is a constant headache for freelance technical writers."

Count: Keeping1 API2 docs3 in4 sync5 with6 code7 is8 a9 constant10 headache11 for12 freelance13 technical14 writers15. => 15 words.

"Every new release risks outdated snippets and missing parameter notes, eroding trust with developers."

Every1 new2 release3 risks4 outdated5 snippets6 and7 missing8 parameter9 notes,10 eroding11 trust12 with13 developers14. => 14 words.

"Automating the detection and update loop turns this reactive chore into a seamless, reliable process."

Automating1 the2 detection3 and4 update5 loop6 turns7 this8 reactive9 chore10 into11 a12 seamless,13 reliable14 process15. => 15 words.

Blank line.

Core Principle: Continuous Sync via Event‑Driven AI Agents

Heading: "## Core Principle: Continuous Sync via Event‑Driven AI Agents"
Words: Core1 Principle:2 Continuous3 Sync4 via5 Event‑Driven6 AI7 Agents8 => 8 words.

Paragraph:

"The key idea is to treat documentation as a downstream consumer of API changes, triggered automatically whenever a new version is published."

The1 key2 idea3 is4 to5 treat6 documentation7 as8 a9? Wait need to count: The(1) key2(2) is3 to4 treat5 documentation6 as7 a8 downstream9 consumer10? Actually "as" is word 8? Let's count: The(1) key2 idea3 is4 to5 treat6 documentation7 as8 a9 downstream10 consumer11 of12 API13 changes,14 triggered15 automatically16 whenever17 a18 new19 version20 is21 published22. => 22 words.

"By hooking into repository events (like a push tag), a workflow fetches the diff, feeds it to an AI summarizer, and creates a ready‑to‑act issue in the docs repo."

By1 hooking2 into3 repository4 events5 (like6 a7 push8 tag),9 a10 workflow11 fetches12 the13 diff,14 feeds15 it16 to17 an18 AI19 summarizer,20 and21 creates22 a23 ready‑to‑act24 issue25 in26 the27 docs28 repo29. => 29 words.

"This closes the loop between code and prose without manual tracking, ensuring that every breaking change, deprecation, or new endpoint surfaces as a concrete task with context."

This1 closes2 the3 loop4 between5 code6 and7 prose8 without9 manual10 tracking,11 ensuring12 that13 every14 breaking15 change,16 deprecation,17 or18 new19 endpoint20 surfaces21 as22 a23 concrete24 task25 with26 context27. => 27 words.

Blank line.

Mini‑Scenario

Heading: "## Mini‑Scenario"
Words: Mini‑Scenario1 => 1? Actually "Mini‑Scenario" as one word.

Paragraph:

"Imagine a freelance writer maintains the docs for a SaaS API."

Imagine1 a2 freelance3 writer4 maintains5 the6 docs7 for8 a9 SaaS10 API11. => 11 words.

"When the team pushes a v2.1 tag, the workflow spots a removed limit parameter, asks the AI to draft a deprecation note, and opens an issue pointing to authentication.md and quickstart.md."

When1 the2 team3 pushes4 a5 v2.16 tag,7 the8 workflow9 spots10 a11 removed12 limit13 parameter,14 asks15 the16 AI