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Working With Long Context

A million tokens is a capability, not a strategy.

AI Builder · Advanced · 14 min read · 4 quiz questions

By Ronan Liedmeier, fractional head of growth and course author · Reviewed 13 October 2026

What you will learn

  • What long context actually buys — and its tax
  • Lost-in-the-middle
  • Chunk-and-summarize chains
  • Progressive summarization for living documents
  • The pointer pattern

Lesson preview

A huge context window means content technically fits, but fitting isn't the same as the model attending to it well — models pay less attention to content buried in the middle of a long context, a phenomenon called lost-in-the-middle. Structured approaches like map-reduce, pointers, and checkpoint summaries beat dumping everything into one giant paste. A model with a very large context window can technically hold hundreds of pages of text at once, and that's genuinely useful — it means you can hand over an entire contract, codebase, or research corpus without pre-chunking it yourself. But there's an attention tax: everything in that context is competing for the model's limited attention, and a single relevant paragraph buried among 200 pages of noise gets diluted. The capability of a large context window and the strategy of how you use it are two different things — having room for a million tokens doesn't mean the best move is filling it. Research and practical experience both show that models tend to pay less attention to content placed in the middle of a long context than content at the beginning or end — a phenomenon called "lost in the middle." This means if the answer to your question happens to sit in the center of a huge pasted document, the model is measurably more likely to miss it or under-weight it compared to the same fact placed near the start or end. 2") compensates for attention that naturally degrades with distance from the edges.

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Key terms in this module

Context window
A context window is the maximum amount of text an AI model can take into account at once, measured in tokens. It holds your messages, any files you add, its instructions and its own replies, and whatever doesn't fit is left out.
Lost in the middle
Lost in the middle is the tendency of AI language models to miss information placed in the middle of a long input while using information at the start or end more reliably. The name comes from a 2023 research paper by Liu and colleagues.

Frequently asked questions

Who is "Working With Long Context" for?
This Advanced module in AI Builder is for learners who already use AI daily and are ready to build their first real workflows and integrations. It takes about 14 minutes and ends with a 4-question quiz.
Do I need to code?
No traditional programming is required. You will use no-code and low-code tools, and occasionally paste a short snippet that the lesson explains line by line.
What comes before and after this module?
Before "Working With Long Context", take "Debugging AI Workflows" (module 12 of AI Builder). Afterwards, continue with "Voice & Image in Workflows".

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