Conclusion

RAG is a major solution for information retrieval in the age of generative AI. Enabling a large language model to generate answers from precise, targeted or private knowledge and documents, RAG fills the gaps left by an LLM trained on data whose origin is not controlled, and which may be inaccurate or have lost their validity. Given the high cost of training or refining a large language model, RAG makes it possible to exploit pre-trained and refined models. It allows a rapid and continuous injection of new knowledge, which a generative LLM can take advantage of, with a view to providing fluid, targeted and motivated responses.

Despite advances in the performance of generative models and RAG solutions, several challenges remain. First of all, it is illusory to imagine a perfect system, which would never generate wrong answers, and which would be unbiased. Large language...