Retrieval-augmented generation (RAG) is essential for most large language model (LLM) applications because it brings company-specific information into the generation process. If you’re planning to deploy generative AI in your organization, you’ll almost certainly need RAG. When done right, it improves accuracy, reduces hallucinations and lets LLMs reason over your own proprietary data. The concept sounds simple: Find information relevant to a user’s query and pass it to the LLM so it can generate a better answer. But as always, the…
