RAG

Retrieval Augmented Generation, or RAG, is a clever way to make large language models (LLMs) smarter and more reliable. Imagine an LLM that knows a lot but sometimes makes up information or is out of date. RAG addresses this by giving the LLM a 'research assistant' that can look up relevant information on demand.

Here's how it generally works: when you ask a question, the RAG system first searches a reliable, external knowledge base (like a database, documents, or the internet) to find relevant pieces of information. Then, it takes your original question and these retrieved facts and feeds them both to the LLM. This way, the LLM isn't just relying on what it learned during training, but also on real-time, specific data, leading to more accurate and informed answers. RAG is particularly useful for applications requiring up-to-date or domain-specific knowledge where LLMs typically struggle.