How to Teach the LLM to Think With Your Data

This approach misses the real strength of LLMs. Instead of exposing raw RAG output, we should feed the retrieval knowledge back into the LLM first. This allows the model to reason with the context, synthesize multiple pieces of information, and deliver answers that are more accurate, natural, and aligned with user intent. In other words, we are not just teaching the LLM facts - we're teaching it to think with our data.