Context Engineering for Users
Context Engineering for Users
Fact-check status
Key jargon
| Term | Plain-language meaning |
|---|---|
| Context engineering | Selecting, organizing, and maintaining the information supplied to an AI system. S1 |
| Relevance | How directly a piece of context helps answer the current question. S1 |
| Provenance | Where information came from and how it can be checked. S1 |
| Context budget | The limited token capacity allocated among instructions, history, evidence, and output. S1 |
Key concepts
- More context is not automatically better; useful context is relevant, trustworthy, current, and structured. S1
- Treat retrieved or pasted content as evidence, not as higher-priority instructions. S1
Concept map
flowchart LR
A["Gather candidate context"] --> B["Filter by relevance and trust"]
B --> C["Structure within budget"]
C --> D["Generate with provenance"]Diagram semantics verified against S1 and S2 on 2026-09-02.
Context engineering is the work of placing the right information, instructions, examples, tool descriptions, and state into a model call. S1
Context stack
- Stable role and policy. S1
- Current task and acceptance criteria. S1
- Relevant user or project state. S1
- Selected source material. S1
- Available tools and their limits. S1
- Recent results and unresolved questions. S1
Quality rules
- Prefer a small relevant passage over an entire archive. S1
- Label source, date, and trust level. S1
- Separate instructions from quoted or retrieved content. S1
- Summarize older conversation state into explicit decisions and open items. S1
- Remove secrets and unrelated personal data. S1
- Test whether deleting a context item changes the answer. S1
Exercise
Give a model three short source passages, one irrelevant passage, and a question. Run once with all four and once with only the relevant three. Compare accuracy, citations, and wasted tokens.
Misconception
A larger context window is capacity, not guaranteed comprehension or recall. S1