🔗 Retrieval Augmented Generation: What It Is and How to Start Using It

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If you've spent any time in working with Large Language Models (LLMs) over the past two years, you've almost certainly heard of Retrieval Augmented Generation (RAG). RAG combines the strengths of information retrieval with the language capabilities of LLMs. RAG as a concept is relatively simple to understand: You retrieve relevant context from some corpus, inject the context into the prompt to your LLM, and have the LLM generate output based on your prompt. Despite its simplicity, designing good RAG solutions remains a challenge. In this post, you'll learn about RAG in detail, and discover some solutions to level up your RAG game.

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