RAG & retrieval
Why do RAG systems fail despite grounding?
RAG's promise is grounding: answers anchored to retrieved, citable text hallucinate less. But it only helps if retrieval surfaces the right passage, which is why most RAG failures trace back to retrieval, not the model.
The promise of RAG is grounding: an answer anchored to retrieved, citable text hallucinates less than one pulled from memory alone. But the promise has a condition. Grounding only helps if retrieval surfaces the passage that actually contains the answer. When the chunks are wrong, the model answers fluently from the wrong source. This is why, in practice, most RAG problems trace back to retrieval, not to the model: the chunk size was off, the embedding match was weak, or the right passage never made the top results.
That is also why the storage layer matters. Independent comparisons of vector databases note that a handful of options cover most needs; Firecrawl’s review concludes that Pinecone, Weaviate, and Qdrant are the strongest choices for most RAG workloads.[1]
References
- Best Vector Databases — Firecrawl