How I Reduced Hallucinations in a RAG System by 60% | Fixing Grounding

Description

In the previous video, we improved retrieval quality by increasing Recall@5. But even with strong retrieval, the model was still generating statements that were not supported by the retrieved context. This video focuses on the second stage of the RAG pipeline: grounding. I walk through how we identified hallucinations in generated responses, how we measured them using sentence-level grounding checks, and the practical changes that reduced unsupported content by roughly 60%. The approach focuses on: Strengthening grounding prompts Reducing context noise Aligning generation behavior with evidence Measuring faithfulness and hallucination rate This is a continuation of the RAG evaluation and optimization series focused on building reliable, production-grade systems. #RAG #GenerativeAI #LLM #AIEngineering #MLOps #VectorSearch #AIObservability #MachineLearning #LangChain #LlamaIndex #AIArchitecture