How Do You Know Your RAG Actually Works?

It's Working Really Well(phase 5A)

The funny thing? I used to think evaluation was something only big companies worried about — something for teams with a dedicated ML platform, not for someone building a demo alone on a weekend. The first time I built a RAG system myself, I just kept asking the same five questions, over and over, until I'd convinced myself it worked. Looking back, I wasn't testing the system. I was testing my own optimism.

There's also a quieter reason this matters. Every time someone tweaks a prompt, swaps the embedding model, upgrades the reranker, changes the chunk size, or imports a fresh batch of documents — any one of those can silently make retrieval worse. Nobody notices, because nobody's watching a number. They find out three weeks later, from a customer complaint. Evaluation is what turns "we think it's still fine" into "we know exactly what changed."

Why Fifteen Random Questions Prove Nothing

OFFLINE EVALUATION PRODUCTION MONITORING

How Do You Know Your RAG Actually Works?

Related reading

RAG-Based Testing Series — Part 1: What Is RAG & Why Your Old Testing Playbook…

My RAG evaluation pipeline returned nan — here's what that taught me about…

RAG-Based Testing Series — Part 2: Testing Retrieval Quality — Are You Fetching…

RAG Evaluation Checklist for AI SaaS: Catch Bad Answers Before Users Do

I Built a RAG App, Then Asked It What Car I Like. It Didn't Know.

Build Your RAG System Right the First Time: 6 Decisions That Make or Break It