Tokens, Context Windows, and RAG: Core AI Concepts Explained for Builders

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A new educational video by two developers breaks down foundational concepts essential for working with large language models. Tokens are described as the basic units of input and output that LLMs process, while context windows define how many tokens a model can handle at once. Retrieval-augmented generation, or RAG, refers to supplying a model with external data relevant to a specific query that was not part of its original training. The video also addresses 'tokenmaxxing,' clarifying that using more tokens does not automatically translate to greater productivity. The creators note that design patterns for providing context to LLMs have evolved rapidly and are expected to continue doing so.

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