Guide / Cheat Sheets

Context Engineering

Curating the token budget: compaction, just-in-time retrieval, agentic memory.

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Agentic Memory

Persistent notes or state an agent writes and reads across steps or sessions so it doesn't have to re-derive the same information.

e.g. A running to-do list the agent updates as it completes steps.

Compaction

Summarizing or discarding older context to free up space in the context window as a conversation or task grows long.

e.g. Replacing 50 old messages with a 3-sentence summary.

Context Engineering

Deliberately curating exactly what information enters a model's limited context window, and in what order, to get better and cheaper results.

e.g. Trimming irrelevant chat history before a new turn.

Context Rot

The tendency for model performance to degrade as irrelevant or stale information accumulates in a long context window.

e.g. An agent losing track of the original goal after 100 tool calls.

Just-in-Time Retrieval

Fetching information only at the moment it is needed, rather than pre-loading everything upfront, to keep context lean.

e.g. Looking up a file's contents only when a task actually references it.

Token Budget

The portion of the context window deliberately allocated to a given kind of content (instructions, history, retrieved docs) so the most important information isn't crowded out.

e.g. Capping retrieved documents to 2,000 tokens so instructions always fit.