A Note on Samples
These tools were built for internal AWS workflows and are not available for external demonstration. What I can share is how they worked, the problems they solved, and the measurable impact they had. The experience I gained building these systems has only deepened since—I continue to explore and build with new AI capabilities as they emerge.
Problem Solved
Technical writers spend significant time transforming raw technical inputs (PRFAQs, design documents, API specifications) into customer-facing documentation. This process involves understanding context, applying style guidelines, and multiple revision cycles.
How It Worked
I built a multi-stage AI pipeline using Amazon Q, LLMs, and MCP (Model Context Protocol) that automated the drafting process from initial draft through post-review revisions:
- Context ingestion: System reads and understands source documents (PRFAQs, engineering specs, API references, etc.)
- Fact-checking layer: Cross-references generated content against source documents to catch hallucinations
- Editorial review: Applies top AWS style guide rules and flags potential issues for human review
- Feedback integration (Quip): Writes to Quip, and after review, incorporates reviewer comments, learns from corrections, and creates new drafts with comment references
- Structured output (XML): Generates documentation following AWS's information architecture, style guidelines, and formatting
Results
- Reduction in time from initial draft to review-ready content and post-review draft
- Consistent application of style guidelines across all generated content
- Reduced cognitive load on writers, allowing more time to focus on technical accuracy and user needs