How to Teach the LLM to Think With Your Data
SMRTR summary
Feeding retrieved data directly back into an LLM — rather than returning raw search chunks to users — transforms a basic RAG pipeline into an intelligent assistant that reasons, synthesizes, and responds naturally. Using Llama 3 8B and Weaviate, this approach reduces hallucinations and eliminates the need for costly fine-tuning.
SMRTR provides this summary for quick context. The original article belongs to Hacker Noon.
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