Context Engineering
After a long conversation, the AI starts forgetting things and going off track. What information should I give it?
After a long conversation, the AI starts forgetting things and going off track. What information should I give it?
First define what this turn must accomplish; otherwise you cannot judge which material is relevant.
Context engineering aims for relevance, clarity, and maintainability. With a limited budget, compression and exclusion matter as much as inclusion.
Keep high-signal material:Prioritize the current goal, hard constraints, confirmed decisions, and necessary tool instructions.
Update as work moves:Retain new decisions and open questions while summarizing repeated chat and old logs.
Retrieve details on demand:Keep paths, queries, or source indexes as pointers so the agent can read the relevant detail when needed.
This task is long, and you have started missing a newly confirmed constraint. Before continuing, prepare a working context with the current goal, protected scope, approved decisions, relevant files, and open issues. List repeated logs and obsolete attempts as material to exclude, while keeping paths for on-demand retrieval. Show me the result for confirmation before proceeding.