Lesson 5 of 12
Structured learning draftContext design with Chain-of-thought
In Applied Prompt Engineering, the way a learner handles context design shapes how Chain-of-thought is used and evaluated. Context design supplies relevant evidence, definitions and examples. This intermediate lesson focuses on a decision or output that another person can inspect.
Learning objectives
- Explain context design in the context of Applied Prompt Engineering.
- Apply Chain-of-thought to a bounded practical task.
- Evaluate the result using explicit quality criteria.
Context Design: from context to evidence
Context Design connects bounded input to a reviewed output in Applied Prompt Engineering.
Define the purpose, intended user and Chain-of-thought constraints.
Separate authoritative context from untrusted material and label each source.
Compare the observed result with a normal case, boundary case and stated limitation.
Context design supplies relevant evidence, definitions and examples. For Chain-of-thought, distinguish performing an operation from demonstrating that it suits the stated purpose. Separate authoritative context from untrusted material and label each source. Record assumptions that could change the conclusion.
Apply context design deliberately
- State the Applied Prompt Engineering task and the decision it supports.
- Prepare a small Chain-of-thought case with a known input and difficult boundary.
- Separate authoritative context from untrusted material and label each source.
- Compare the observed result with the expected behaviour and explain differences.
- Save the evidence, limitation and next action in a review record.
A worked Chain-of-thought evidence path
A four-step worked example for applying context design to Chain-of-thought, including a boundary test and revision.
Preserve the original Chain-of-thought case and expected result.
Confirm the basic path behaves as expected.
Expose an assumption in the context design method.
Change the method, rerun both cases and record the limitation.
| Review point | Evidence |
|---|---|
| Purpose | The specific Chain-of-thought outcome and intended user |
| Method | The context design decision, input and version or context |
| Result | Observed output plus a checked boundary case |
| Limitation | What the result does not establish and the next safe action |
Common mistakes
- Using Chain-of-thought before defining what context design must achieve.
- Checking only the easiest Applied Prompt Engineering example.
- Reporting a result without its input, assumptions or limitation.
Practice activity
Apply the lesson
For Applied Prompt Engineering, complete a bounded Chain-of-thought task demonstrating context design. Keep the original input, numbered method, normal test, boundary test, observed results and a 100-word self-review naming one limitation and next improvement.
Check your understanding
In Applied Prompt Engineering, which evidence best supports a context design result produced with Chain-of-thought?
Lesson summary
- For Applied Prompt Engineering, context design means: Context design supplies relevant evidence, definitions and examples.
- A credible Chain-of-thought result includes a checked boundary, not only a successful example.
- The next lesson builds on this context design evidence record.
Sources and further reading
- AI Risk Management Framework 1.0NIST - accessed 2026-08-21
- AI PrinciplesOECD - accessed 2026-08-21
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