If your retriever is broken, your entire RAG system is broken. Learn how to measure retrieval quality using real metrics — Precision@K, Recall@K, MRR, and NDCG — and write your first actual retrieval tests in Python.
RAG-Based Testing Series — Part 2: Testing Retrieval Quality — Are You Fetching the Right Data?
"A RAG system is only as good as what it retrieves. Get retrieval wrong — and everything that follows is built on sand."
In Part 1, we established the big picture.
We learned what RAG is, why traditional testing breaks down with AI systems, and what the major failure modes look like.
If you haven't read Part 1 yet — go read it first. This series builds on itself. 🔗
A complete beginner-friendly breakdown of RAG systems, why they need a completely different testing approach, and what's coming…
Happy path testing isn't enough. Learn the edge cases that silently break RAG systems in production — empty knowledge bases,…
A practical RAG evaluation checklist for AI SaaS builders to test retrieval quality, grounded answers, citations, regressions,…
Why modern AI coding agents often use grep, file reads, symbols, and tests before reaching for vector RAG.
RAG sounds complicated. It's not. But a lot of introductions to RAG make it sound more mysterious...
RAG (Retrieval-Augmented Generation) is the foundation of knowledge-grounded AI. But most RAG...
