I built a RAG system last year that worked perfectly on my test data.

Deployed it. Client called within a week: "It's giving completely wrong answers about our Q3 revenue."

Turned out my chunking strategy was splitting financial tables right down the middle. Half the revenue data ended up in one chunk, half in another. The retrieval grabbed one half, and the LLM confidently made up the rest.

This is why RAG is deceptively hard.

The concept is simple — retrieve relevant documents, feed them to an LLM, get grounded answers. The reality is a minefield of chunking edge cases, embedding quality issues, retrieval failures, and the LLM still hallucinating even WITH the right context.

What actually moves the needle: hybrid search (semantic + keyword), re-ranking with cross-encoders, thoughtful chunk overlap, and aggressive evaluation with RAGAS.

Everyone's building RAG apps in 2026. Very few are building GOOD ones. That's your opportunity.