RAG Powered LLMs for QA: Evolution, Challenges, Applications, and Future Directions

Large language models (LLMs) are evolving to excel in challenging tasks such as text generation, mathematical reasoning, code generation, question answering, text summarization, etc. However, the responses generated by LLMs are prone to hallucinations, out-of-the-date knowledge, and nontransparent and untraceable reasoning. Retrieval augmented generation (RAG) addresses these shortcomings by incorporating external knowledge in the LLM prompt. RAG combines parametric knowledge of LLM with non-parametric knowledge from external databases by efficient retrieval techniques. This comprehensive review paper provides a detailed overview of the significance and emergence of RAG. Moreover, the building blocks of RAG are thoroughly discussed along with the evolution, challenges, and current research trends of deploying RAG-based applications. Finally, we explore the RAG performance enhancement strategies while also underlining the potential future research directions.