RAG hallucinations often come from wrong or missing chunks — not from the model “making things up” in isolation. Experts stress missing data, bad chunking, weak embeddings, and retrieval that ignores the best passages.
RAG hallucinations often come from wrong or missing chunks — not from the model “making things up” in isolation. Experts stress missing data, bad chunking, weak embeddings, and retrieval that ignores the best passages.
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"You might be missing data. You might be chunking them in the wrong way. You might be using an embedding model that isn't optimum. Maybe your retrieval strategy needs to change."
Practitioner clips ground architecture decisions in how retrieval systems fail and get evaluated in production.
Practitioner clips ground architecture decisions in how retrieval systems fail and get evaluated in production. Signals: clean transcript excerpt, recognized expert channel.
Source credibility
Pinecone
Webinar: Fix Hallucinations in RAG Systems with Pinecone and Galileo
19:48
Vendor engineering content on retrieval and vector search.
There are a few metrics, but the most important one for us is “Recall.” Basically, for a given question, there is at least one required fact. If the retrieval step of the application found at least one context for every required fact, we mark that for a set of questions.
•Context ignored — model answers from parametric memory despite retrieval.
•Conflicting passages merged into one summary.
•Treating RAG as a magic prompt wrapper without measuring retrieval recall on real questions.
•Skipping chunking strategy because the context window is large.
•Picking an embedding model that mismatches domain vocabulary without offline recall checks.
Implementation tradeoffs
•Reranking: Cross-encoder or LLM rerankers improve top-k quality at higher latency and inference cost.
•Knowledge updates: RAG re-index cadence vs fine-tune retrain cycles when policies or product facts change frequently.
•Regression testing: Fine-tune releases need behavior suites on fixed prompts; RAG releases need recall suites on labeled questions — teams often test only one.
Themes repeated across indexed engineering talks and practitioner writeups — not a survey, vote count, or attributed quote roundup.
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