Enhancing the RAG Pipeline Through Advanced Optimization Techniques
Qazi Mudassar Ilyas, Sadia Aziz · Advances in computational intelligence and robotics book series · 2025
Large language models produce excellent outputs for queries highly relevant to their training data. Retrieval-augmented generation (RAG) is used to augment this training data with additional contextual information based on additional data. Although RAG improves text generation through context retrieval from this additional data, the basic RAG system has limitations in chunking, hallucinations, and reliance on augmented content for knowledge-intensive tasks. This chapter discusses several advanced techniques to enhance retrieval and generation tasks in an RAG pipeline. The chapter discusses advanced strategies for chunking, vectorization, and search processes. Moreover, reranking, filtering, query transformation, query routing, and response synthesis improve generated responses' relevance, coherence, and accuracy.