ML Engineering

A three-part build log for creating an AI-agent-queryable technical documentation system: reliable ingestion, high-precision retrieval, and measurable evaluation.

• The Pipeline Problem: Building the Data Foundation for an Agent That Doesn't Lie

  Published on:January 14, 2026

  A story about graduating from ad-hoc ingestion scripts to a real distributed file-processing platform using Ray and Azure, and the hard lessons learned along the way.

• RAG in the Real World: Building a High-Precision Retrieval System for LLM Pipelines

  Published on:February 3, 2026

  Building a high-precision retrieval system sounds simple on paper. This is what actually happens when you ship one — the chunking failures, the retrieval gaps, and the hybrid search that finally gave us reliable signal.

• You Can't Improve What You Don't Measure: Evaluating Knowledge-Grounded Retrieval Systems

  Published on:February 24, 2026

  After shipping a RAG retrieval system, the hardest question is whether it actually works. This post covers RAGAS, LLM-as-judge calibration, the evaluation triad, and how to build an eval pipeline that gives you real signal.