Prompt patterns that produce valid IOModel output.
Good prompts get good structure. Because AI generates against the IOModel schema and validates its output, a few patterns make results reliable and easy to review.
Core patterns
| Pattern | Why it helps |
|---|---|
| Decompose top-down | Containers → subsystems → components/APIs keeps structure clean |
| Reference the schema | Output conforms to allowed types and properties |
| Validate after each step | Catches inconsistencies before they compound |
| Use dry-run for mutations | Preview changes before they are written |
| Work in small steps | Smaller diffs are easier to review and accept |
A reliable request shape
State the goal
Describe the system and its key inputs/outputs in plain language.
Set constraints
Name the schema, target model, and any required types or tech.
Ask for decomposition
Request a top-down breakdown rather than one flat dump.
Require validation
Ask AI to validate and fix diagnostics before finishing.
Example prompt
Design a service that transcribes a gRPC audio stream to text.
- Use the IOModel schema (objects + links).
- Decompose top-down: system → containers → components/APIs.
- After each step, validate and fix any diagnostics.
- Finish with an overview spec page that embeds the model.Tell AI to stop and show a plan (dry-run) before applying large changes. Reviewing the plan is faster than untangling a big unwanted change.
Anti-patterns
- Asking for everything in one giant prompt.
- Skipping validation and trusting raw output.
- Letting AI invent types or fields outside the schema.
Keep it grounded
If AI starts producing free-form structure, redirect it to the schema and ask it to validate. Grounding is what makes the output trustworthy.
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