AI guides
GPT-5.6 Tools and Function Calling: Design for Reliable Results
Build reliable AI tool workflows with narrow tool definitions, validation, and human-safe fallbacks.
A model should not receive broad authority by default. Keep tool descriptions specific, validate every input server-side, and make risky actions reviewable. Tool calling is most reliable when the model has a small set of well-defined choices.
Key takeaways
A practical workflow
- 1Define the exact outcome you want from Design for Reliable Results.
- 2Give the AI the necessary context, constraints, examples, and preferred format.
- 3Review the result for accuracy, tone, privacy, and completeness before using it.
- 4Save or reuse the prompt only after it produces a reliable result for your use case.
Put this into practice
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Format tool JSONCommon mistakes to avoid
- Letting the model decide access control.
- Using vague tool descriptions that overlap.
Official sources and further reading
AI products, model availability, and pricing change frequently. Check these primary sources before making a decision.
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Frequently asked questions
What should I check first when using Design for Reliable Results?+
Give each tool one clear responsibility. Start with the official source or your own verified input, then decide whether AI is appropriate for the task.
How do I get a more useful result from Design for Reliable Results?+
Validate parameters and permissions outside the model. Give the AI a specific goal, relevant context, constraints, and the format you want back; then review the output before using it.
What is the key mistake to avoid with Design for Reliable Results?+
Letting the model decide access control. AI can accelerate drafting and analysis, but important facts, decisions, and sensitive work still need human review.