What’s the most effective way to reduce hallucinations in enterprise RAG...

vegan_vicky

We’ve been testing different approaches for reducing hallucinations in production-grade LLM applications, especially in retrieval-augmented generation (RAG) systems used for enterprise workflows. Some observations from our experiments: Hybrid search (BM25 + vector retrieval) performs better than pure semantic retrieval for factual enterprise data Chunk size significantly affects answer accuracy and token cost Smaller task-specific models often...

Originally asked by Ritu Singh on Stack Exchange (ai) (CC BY-SA 4.0).

uphilltech

Great question — answering as the UphillTech team:

What retrieval strategy is working best for your RAG systems? Mine use a Hybrid retrieval combining BM25 keyword search with dense vector search. It may work different for you but in my case it improves factual grounding. You can try using Elasticsearch with vector embeddings and reranking pipelines. Are you using open-source or managed vector databases? I prefer Pinecone , but Qdrant is also a good open-source alternative. How are you measuring hallucination rates in production? The most reliable way my team use to measure hallucination rates is through automated grounding evaluation combined with human review sampling. Typically we compare generated answers against retrieved source...

Full answer by Stewie pixel: https://ai.stackexchange.com/a/50578 (CC BY-SA 4.0).

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