Advanced RAG techniques

While basic RAG pipelines involve retrieving relevant documents and directly providing them as context to the LLM, advanced RAG techniques employ various methods to enhance the quality, relevance, and factual accuracy of generated responses. These advanced techniques go beyond the naive approach of simple document retrieval and context augmentation, aiming to optimize various stages of the RAG pipeline for improved performance.

Let’s now look at some key areas where advanced RAG techniques focus.

Query handler – query reformulation and expansion

One key area of advancement is query reformulation and expansion. Instead of relying solely on the user’s initial query, advanced RAG systems employ NLP techniques to generate additional related queries. This increases the chances of retrieving a more comprehensive set of relevant information from the knowledge base. Query reformulation can involve techniques such as the following:

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