How to improve RAG results in your LLM apps: from basics to advanced

If you’re building any meaningful product/feature with LLMs (large language models), you’ll probably use the technique called RAG (retrieval-augmented generation). It can allow you to integrate external data that was not available in the LLM’s training data into the LLM’s text generation process, which can greatly reduce the nightmare of hallucination and improve the relevance of the text responses.

The idea of RAG seems simple enough: find and retrieve the most relevant text chunk and plug it in the original…