RAG, which stands for "Retrieval Augmented Generation," is a strategy in artificial intelligence where a large language model (LLM) retrieves relevant information from an external knowledge base (like a database or document collection) before generating a response to a user query, ensuring the response is more accurate and contextually relevant by... See more

Goodbye, vanilla RAG. Hello, Agentic RAG! 𝗩𝗮𝗻𝗶𝗹𝗹𝗮 𝗥𝗔𝗚 The common vanilla RAG implementation processed the user query through a retrieval and generation pipeline to generate a response grounded in external knowledge. Advanced vanilla RAG techniques... See more

• Query the RAG anyway and let the LLM itself chose whether to use the the RAG context or its built in knowledge


• Query the RAG but only provide the result to the LLM if it meets some level of relevancy (ie embedding distance) to the question


• Run the LLM both on it's own and with the RAG response, use a heuristic (or another LLM) to pick the best answer

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