Context, Constraints, and Examples
What may the AI produce, what must it never decide, and what evidence lets a person approve the result? Provide only relevant context, make hard boundaries explicit, and use examples to clarify rather than secretly replace requirements.
What You Will Be Able to Decide
- Explain context, constraints, and examples in product and business terms.
- Apply this decision: Provide only relevant context, make hard boundaries explicit, and use examples to clarify rather than secretly replace requirements.
- Recognise this material risk: the model fills missing decisions with plausible assumptions that conflict with the product.
- Use this review: Run one customer email through the intake tool with missing context, ambiguous intent, and a request that needs human approval.
A founder is deciding what to delegate to AI and what evidence to require before accepting the result. This lesson gives you a concrete question to take into a build brief, proposal review, or product decision.
What may the AI produce, what must it never decide, and what evidence lets a person approve the result? The course example is An AI assisted intake tool that turns customer emails into draft tasks; use it to decide what evidence would justify the choice before a builder implements it.
What Does Context, Constraints, and Examples Mean for Your Product?
A founder is deciding what to delegate to AI and what evidence to require before accepting the result.
Use the illustrative service for this course (An AI assisted intake tool that turns customer emails into draft tasks) to make the choice concrete. What may the AI produce, what must it never decide, and what evidence lets a person approve the result?
Technical term
Context, Constraints, and Examples
Context explains the situation, constraints define permitted boundaries, and examples demonstrate the intended shape or standard of an output.
How Should a Founder Use Context, Constraints, and Examples?
For an ai assisted intake tool that turns customer emails into draft tasks, ask what would happen if the model fills missing decisions with plausible assumptions that conflict with the product.
For this decision, the useful standard is that the output satisfies explicit constraints and survives review outside the conversation that produced it.
- Decision: Provide only relevant context, make hard boundaries explicit, and use examples to clarify rather than secretly replace requirements.
- Evidence to request: show that the output satisfies explicit constraints and survives review outside the conversation that produced it.
- Owner: name who will respond if the model fills missing decisions with plausible assumptions that conflict with the product.
- Record the result in the AI work brief, review record, and acceptance criteria.
- Practical review: Run one customer email through the intake tool with missing context, ambiguous intent, and a request that needs human approval.
How Do You Choose an Approach to Context, Constraints, and Examples?
What may the AI produce, what must it never decide, and what evidence lets a person approve the result? Provide only relevant context, make hard boundaries explicit, and use examples to clarify rather than secretly replace requirements.
The risk is that the model fills missing decisions with plausible assumptions that conflict with the product. Compare a simpler option with the proposed one, including who will operate either choice.
- Describe the user or business outcome that must be protected.
- Identify the most credible failure and its consequence.
- Compare the simplest adequate approach with one realistic alternative.
- Set a review point for when the decision may need to change.
What Evidence Should You Accept for Context, Constraints, and Examples?
What Warning Signs Should You Look For?
- The proposal does not address this risk: the model fills missing decisions with plausible assumptions that conflict with the product.
- Nobody can show whether the output satisfies explicit constraints and survives review outside the conversation that produced it.
- The decision has no named owner or review point.
What Should You Ask a Consultant?
- What changes for the user if we choose this approach to context, constraints, and examples?
- How have we reduced or accepted this risk: the model fills missing decisions with plausible assumptions that conflict with the product.
- Can you demonstrate that the output satisfies explicit constraints and survives review outside the conversation that produced it?
- Who owns the result, and when will we reconsider it?
Key takeaway
Key Takeaway
Provide only relevant context, make hard boundaries explicit, and use examples to clarify rather than secretly replace requirements. Ask for evidence against the specific risk: the model fills missing decisions with plausible assumptions that conflict with the product.