Lightning Talk: Grounding LLM to Avoid Hallucinations Using Vector S...
summary
Claims that grounding LLMs with embeddings plus vector search eliminates hallucinations and provides auditable citations for enterprise use cases such as CRM search and customer service chatbots. Converts documents into embeddings with sentence-based chunking or simple 200-character chunks or a parent document retriever, stores them in a vector database, retrieves top-K nearest neighbors, concatenates those passages with the user query, and sends that context to the LLM. Reported as a practical RAG pipeline used successfully for Q&A and content tasks.
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