Context pinning
Context pinning explicitly keeps critical pieces of information at the head or tail of an agent's prompt across many turns — defending against the lost-in-the-middle recall problem on long contexts.
Models recall information at the head and tail of long contexts much better than in the middle. Context pinning is the production discipline of explicitly anchoring critical content at those positions. Implementations: a fixed "system + critical state" block always at the head; an "open subtasks + recent decisions" summary always near the tail. Used across long agent loops, multi-turn assistants, and RAG over long documents. Best practice in 2026 is to refresh the pinned content during conversation compaction so it stays accurate.
When to use context pinning
- Long agent loops with critical state.
- Multi-turn assistants with returning users.
- RAG over long documents with task-relevant constants.
Common mistakes
- Pinning too much — context bloat defeats the purpose.
- Letting pinned content drift out of sync with current state.
FAQ
What is context pinning?
Context pinning explicitly keeps critical pieces of information at the head or tail of an agent's prompt across many turns — defending against the lost-in-the-middle recall problem on long contexts.
When should I use context pinning?
Long agent loops with critical state. Multi-turn assistants with returning users. RAG over long documents with task-relevant constants.
What are the most common mistakes with context pinning?
Pinning too much — context bloat defeats the purpose. Letting pinned content drift out of sync with current state.
Related terms
- 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.
- Context distillation — Context distillation summarises an agent's growing conversation history into a compact representation, so each step's input stays small while preserving the relevant signal.
- Conversation compaction — Conversation compaction summarises a long agent or chat history into a tight representation that preserves the relevant signal — used when the conversation approaches the model's context window.
- System message — A system message is the highest-priority instruction message in a chat-style API call — used to set role, constraints, and behaviour for the entire conversation.
Last updated: 2026-06-01. Raw markdown: https://promtable.com/glossary/context-pinning.md.