From the course: Introduction to Large Language Models (LLMs) and Prompt Engineering by Pearson

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Introduction to retrieval-augmented generation (RAG)

Introduction to retrieval-augmented generation (RAG)

From the course: Introduction to Large Language Models (LLMs) and Prompt Engineering by Pearson

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Introduction to retrieval-augmented generation (RAG)

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It's time to put a lot of what we learned in the last couple of sessions into a real-life application. We've seen semantic search and we've seen how prompt engineering can drastically change the outputs of our generative AIs. Putting these two together often results in a system called retrieval augmented generation or RAG. A RAG system, at its simplest, is an interactive application, usually a chatbot, but technically not always, that combines retrieved and generally static information, i.e. semantic search, with a dynamic conversation generator, in our case, generative AI, like a GPT, CLAWD, COHERE, LLAMA, name it. The premise of how this is going to work, this figure looks familiar, it's because it's effectively a ripoff of the semantic search system that I showed you a couple lessons ago. A basic RAG system will involve someone asking a question or a query on the left, the system retrieving results, i.e. through semantic search from some Pinecone database, other vector database…

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