AI Engineering Lab

Build AI systems with traceable sources, measurable quality, and explicit failure handling.

The AI Engineering Lab documents the methods behind my RAG consulting work: how to build reliable retrieval systems, verify them with evidence, and teach the workflow through practical experiments.

Discuss an AI project Browse the methods

$ lab --method
▸ build  source-aware RAG systems
▸ measureretrieval + generation quality
▸ learn  a 30-day engineering path
● open methods for reliable AI

Build → Measure → Learn

Three projects covering construction, evaluation, and training.

Build the system, measure its behavior, and turn the results into a repeatable learning path.

Open source · Method

RAG Engineer

Build, debug, review, and operate reliable RAG systems from source documents to cited answers.

Explore the method

Open source · Method

RAG Evaluation

Measure AI quality with traces, metrics, validated judges, and readiness audits.

Explore the method

Course in development

Future course · 16 lessons

RAG Formation

A project-based course in development for building a reliable company RAG chatbot through experiments and engineering decisions.

View the course roadmap

Supplementary lesson

RAG Formation Continue · 01 · Production improvement loop

Turn interaction logs and user feedback into an evidence-backed RAG improvement plan with Claude Code or Codex.

Open lesson

Supplementary lesson

RAG Formation Continue · 02 · Build your own RAG chatbot from zero

Build a four-channel RAG baseline with grounded answers, cited sources, and honest refusals—before adding feedback or follow-up handling.

Open lesson

Consulting in practice

Methods used in client delivery.

These public projects support the same goal as my client work: AI systems with traceable outputs, measurable quality, and defined operating limits.

Discuss a corporate RAG system View production work