RAG Techniques: How Query Transformation and Expansion Improve AI Retrieval

In Retrieval-Augmented Generation (RAG) systems, query transformation rewrites vague or incomplete user queries into more specific ones using context already available to the LLM, improving document retrieval accuracy. Query expansion complements this by generating multiple variations of the original query, each used to fetch relevant documents from a vector database. For example, a query like 'How do I deploy a FastAPI application?' can be expanded into variations covering Docker, AWS, Gunicorn, and Uvicorn deployments. During the augmentation phase, all retrieved contexts are combined with the original query and passed to the LLM to generate more accurate responses. As an alternative to LLM-based expansion, a rule-based approach can store pre-clustered related queries in the vector database for faster, cost-effective retrieval.