RAG Tip — Missed The Top Ranked Documents
RAG which is short for Retrieval-Augmented Generation is a powerful technique that combines the natural language understanding and generation capabilities of Large Language Models (LLMs) with the information retrieval abilities of search engines.
RAG allows LLMs to access external sources of knowledge, such as the internet or vector databases, to augment their responses to user queries.
However, RAG is not a magic bullet that can solve all the challenges of LLMs. One of the common issues that RAG users may encounter is missing the top ranked documents from the search results. This means that the LLM may not use the most relevant or accurate information to generate its response, leading to suboptimal outputs.
Why does RAG miss the top ranked documents?
There are several factors that may contribute to RAG missing the top ranked documents from the search results.
In below video, I will introduce you to some of the possible solutions for this problem and provide some tips on how to improve the quality and reliability of RAG-based LLM applications.
