Fix RAG Hallucinations

The Problem with Hallucinations

RAG (Retrieval-Augmented Generation) is powerful, but it often fails when the retrieval is weak or the model ignores the context. This leads to confident but incorrect answers that erode user trust.

How We Fix It

We don't just "try a different prompt." We systematically improve your RAG pipeline:

• Hybrid Retrieval: Combining vector search with keyword/BM25 search for better keyword matching.
• Reranking: Implementing cross-encoders to ensure the most relevant documents are actually at the top.
• Self-Correction Layers: Models that check their own work against the source documents before final output.
• Citation Enforcement: Forcing the model to link every claim to a specific document chunk.

The Result

A reliable, production-ready AI assistant that admits when it doesn't know the answer instead of hallucinating.