The original title is "AI builder essentials: tokens, context windows and RAG 101"

Short summary

A brief introductory post explaining core LLM concepts: tokens as the basic input/output unit, context windows as token capacity limits, and RAG as a method for providing models with relevant external data. The authors also introduce 'tokenmaxxing' — the idea that using more tokens doesn't equate to productivity. Content is foundational and aimed at beginners.

• •Tokens are the basic unit LLMs operate on for input and output
• •Context windows define how many tokens a model can process, and have grown significantly
• •RAG supplies models with external data relevant to a specific query

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