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Technical writer · Seattle, WA
Developer docs people trust and machines can use.
I'm Zain Haq, a technical writer at Amazon Supply Chain Services. I've documented APIs, developer tools, and cloud platforms for more than seven years. I also build AI tools that keep documentation accurate, because a growing share of documentation's readers are now AI agents that act on what the page says.
01 Selected work
Writing samples
These are published documents, recreated with fictional product names. You can read each sample in the browser, download it as a PDF, or fetch it as raw Markdown.
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02 Featured project
AI-powered documentation quality scoring
AI helps teams produce more documentation, and more drafts mean more small errors for reviewers to catch. At Amazon, I built a VS Code extension that scores a documentation draft out of 5. The score combines editorial quality with technical accuracy checks against approved source material. A companion CLI scores web pages and Word documents in batches, so a team can measure quality across its entire library.
The tool gives reviewers a better starting point rather than replacing peer review. I calibrated it against documents I had already reviewed by hand and adjusted the scoring until its feedback matched what a strong reviewer would flag. To control costs, it reserves Claude for the checks that need judgment and leaves the rest to rule-based checks. I built it around the team's review process rather than my own habits.
Read the public write-up- Form factor
- VS Code extension + CLI
- Model
- Claude Sonnet, used selectively
- Output
- Score out of 5, with the issues behind it
- Calibration
- Tuned against hand-reviewed documents
- Philosophy
- Human in the loop. AI assists review; writers decide.
03 Approach
How I work
01
Test first, write second
I test the product hands-on and read the source code when the spec is ambiguous. A claim doesn't go into the docs until I've seen it work.
02
Docs are code
Content lives in Git and ships through review and CI, like the product it documents. At H2O.ai, I owned the publishing pipeline: I led the docs migration to Docusaurus and connected the build to CI/CD. This site works the same way: every sample is a Markdown file that builds into the page you're reading.
03
Write for humans and machines
Documentation now has two audiences. When terminology stays consistent and headings stay exact, AI agents and retrieval systems surface the right answer for your users. Every sample here has a Markdown twin, and the site publishes llms.txt. The same structure makes content work with retrieval-augmented generation (RAG) and Model Context Protocol (MCP) servers.
04
Give AI a map
A model assembles what already exists. Deciding what's worth building still takes a writer who knows the product and the reader. When a model drafts for me, I expect most of it to be sound, verify it with automatic checks, and review the result the way I'd review a colleague's draft.
04 Background
About
I'm a technical writer with more than seven years of experience documenting APIs, developer tools, and cloud infrastructure. The job now has two sides, and I work both: writing the explanations that show how a product fits together, and building the systems that produce and check content with AI. At Amazon, I write documentation for Amazon Supply Chain Services and build tools that improve content quality across the team's docs, including the AI scoring tool featured above.
Before Amazon, I spent five years at H2O.ai documenting machine learning platforms and running the team's documentation platform. Owning a publishing system end to end, from Git to the published page, still shapes how I write.
Teams that work with me get direct questions and documentation feedback that engineers can act on. The goal is clear, accurate content organized around how people actually use the product.
05 Get in touch
Contact
Have an interesting documentation problem? I'd like to hear about it.