Building AI Agents with LLMs, RAG, and Knowledge Graphs

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Extending Your Agent with RAG to Prevent Hallucinations

In earlier chapters, we saw what an LLM is, and in the previous chapter, we saw how it can control different tools to succeed at completing a task. However, some of the limitations of LLMs prevent their deployment in sensitive fields such as medicine. For example, LLMs crystallize their knowledge at the time of training, and rapidly developing fields such as medical sciences cause this knowledge to be outdated in a short time. Another problem that has emerged with the use of LLMs is that they can often hallucinate (produce answers that contain factual or conceptual errors). To overcome these limitations, a new paradigm has emerged: retrieval-augmented generation (RAG). RAG, as we will ...