# Long-context prompting

**Source:** https://promtable.com/glossary/long-context-prompting

> Long-context prompting is the discipline of writing prompts that exploit 200K-1M+ token windows effectively — chunk ordering, head-and-tail anchoring, summarisation, and recall-aware structure.

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Long-context prompting is the discipline of writing prompts that exploit 200K-1M+ token windows effectively — chunk ordering, head-and-tail anchoring, summarisation, and recall-aware structure.

Long context is now the default in 2026 (Claude 200K, GPT-4o 128K, Gemini 2 Pro 1M). But raw long context is not free quality — models still suffer from "lost in the middle" recall degradation. Effective long-context prompting puts critical content at the head and tail, summarises mid-context content explicitly, repeats key instructions near the end of the prompt, and uses long-context evals (needle-in-haystack tests on your data) to verify recall before shipping. Long context also enables many-shot in-context learning — hundreds of examples in the prompt — which can approach fine-tune quality for narrow tasks.

## When to use

- Document QA, summarisation, code-base review.
- Many-shot in-context learning.
- Long agent loops without retrieval.

## Common mistakes

- Trusting public benchmarks instead of needle-in-haystack on your own data.
- Forgetting that long prompts blow up cost — the whole context is billed every turn.

## Related terms

- [context-window](https://promtable.com/glossary/context-window)
- [prompt-caching](https://promtable.com/glossary/prompt-caching)
- [few-shot-prompting](https://promtable.com/glossary/few-shot-prompting)
- [rag](https://promtable.com/glossary/rag)

*Last updated: 2026-06-01*
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Original page: https://promtable.com/glossary/long-context-prompting
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