Boosting Retrieval in RAG Systems with Query Expansion

If you ever work on RAG systems, you must have seen ridiculously vague user queries. My classic version is “What is this paper about?” 😜

In the world of conversational AI, Retrieval-Augmented Generation (RAG) systems have emerged as a powerful approach to enhance the quality and accuracy of responses. These systems rely heavily on the retrieval component to provide relevant context to the language model, which then generates the final output.

However, the performance of the retrieval component can be heavily influenced by the quality of the user’s initial query. This is where query expansion comes into play, offering a way to improve the retrieval process and ultimately enhance the overall performance of RAG systems.

Understanding Query Expansion

Query expansion is the process of enhancing the original user query by adding more relevant terms or phrases. This is particularly useful when the user’s initial query is vague or incomplete, as is often the case when people search using just a few keywords. By expanding the query, we can provide the retrieval component with more context, increasing the chances of finding the most relevant documents.

The key benefit of query expansion is that it helps address the limitations of…