Retrieval-Augmented Generation, Without the Hype
RAG is not a silver bullet for hallucinations. It's a way to ground a model in facts you control — and it has sharp edges of its own.
Retrieval-augmented generation gets pitched as the fix for hallucinations, but in practice it just changes what can go wrong. Instead of a model inventing facts outright, you now have to worry about retrieval quality, chunking strategy, and whether your embeddings actually capture the distinctions that matter for your domain. RAG doesn't eliminate the failure mode — it relocates it upstream, into search.
Treat it as a search problem first
The teams that get the most value from RAG treat it as an information-retrieval problem first and a generation problem second. That means investing in evaluation sets the same way a search team would: a labeled set of realistic queries with known-good source documents, and a way to score retrieval precision independently from the quality of the final generated answer.
- Chunking strategy matters more than model choice — bad chunk boundaries produce confidently wrong answers regardless of which LLM sits on top.
- Hybrid retrieval (keyword plus embedding) consistently outperforms pure vector search on domain-specific terminology.
- Citation surfacing — showing users exactly which source a claim came from — catches more retrieval failures than any automated eval we've built.
Where RAG genuinely struggles
Some query types are a poor fit no matter how good your retrieval is: anything requiring synthesis across dozens of documents, anything where the answer depends on information that changed since your last index, and anything where the user's real question isn't well represented by their literal query. For those cases, we've had more success routing to a different tool entirely than trying to force RAG to do something it's structurally bad at.
The honest pitch for RAG isn't 'it stops hallucinations.' It's 'it lets you ground a model in facts you control, with sharp edges you need to actively manage.' Clients who hear the second version build systems that hold up. Clients who hear the first version are usually disappointed within a quarter.