Context Engineering for Users

Context Engineering for Users

Fact-check status

Reviewed claim by claim on 2026-09-02 against the primary or official sources identified inline as S1 and S2. Recommendations are design guidance unless a cited standard makes them normative.

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

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

  1. Stable role and policy. S1
  2. Current task and acceptance criteria. S1
  3. Relevant user or project state. S1
  4. Selected source material. S1
  5. Available tools and their limits. S1
  6. Recent results and unresolved questions. S1

Quality rules

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