Context Engineering
Context Engineering selects and orders what a model receives. This can include facts, instructions, tools, state, and evidence.
Another example
A support agent gets the current ticket, verified facts, two policies, allowed actions, and a short history summary.
Main parts
- 01User intent and context
- 02Model or tool decision
- 03Grounded result and fallback
Use it when
Use it to keep model work relevant, current, checkable, token-light, and within permission.
Do not use it when
Do not dump every document into context or mix trusted rules with untrusted content. Length cannot replace good selection.
Name used in code
select + structure + prioritize + refresh context Before you ship
Check the model and prompt versions, source data, versioned evaluation set, and measures. Verify links to evidence, tool permissions, privacy, common failures, decline-to-answer and fallback behavior, human review, monitoring, cost, speed, and rollback.
Outcome: Use or evaluate Context Engineering to make AI behavior measurable and tied to evidence. User context: A support agent gets the current ticket, verified facts, two policies, allowed actions, and a short history summary. AI method or concept: Context Engineering. Why it fits: Use it to keep model work relevant, current, checkable, token-light, and within permission. Do not use it when: Do not dump every document into context or mix trusted rules with untrusted content. Length cannot replace good selection. AI requirements: Define the input and source evidence. Set model and tool permissions. Use versioned evaluation data and measures. Define failure, decline-to-answer, privacy, speed, and cost limits. Operational safeguards: Keep a clear trace and hide sensitive log data. Show users a safe fallback. Mark steps that need human review. Define how to roll back the model or prompt. Acceptance criteria: Record baseline and target measures on a versioned evaluation set. Test edge cases and hostile inputs. Verify fallback, monitoring, permissions, and rollback. Evidence and limits (evidence boundary): No single official standard controls the whole term. Its meaning may change across teams, platforms, or frameworks. Unknowns to confirm: Target task, model and version, evaluation owner, source data, risk limit, tool permissions, and production fallback.
Check this request
Review the current use of Context Engineering. Definition: Context Engineering selects and orders what a model receives. This can include facts, instructions, tools, state, and evidence. Release checks: Check the model and prompt versions, source data, versioned evaluation set, and measures. Verify links to evidence, tool permissions, privacy, common failures, decline-to-answer and fallback behavior, human review, monitoring, cost, speed, and rollback. Before changing code, report the evidence you found, gaps, severity, and the smallest safe fix.