Final artifact
An output contract spec, a completed schema review checklist, and an explicit failure policy.
Assessment
This lesson forces you to convert one real task into a machine-verifiable output interface. DepthPilot is not focused on whether the model can emit JSON. It is focused on whether fields, types, failure paths, and downstream consumption are explicit enough to trust.
An output contract spec, a completed schema review checklist, and an explicit failure policy.
Not that the model usually looks like JSON, but that downstream code can validate fields, reject bad results, and decide when to retry or stop without guessing.
This page turns the framing order, contract ladder, bad-schema patterns, and reusable templates into an internal runbook.
Decide whether this is a data-return task or a tool-action task first.
Define what the downstream system needs in fields, types, and failure semantics before writing the prompt.
Separate style, policy, and schema instead of mixing them into one vague instruction block.
Only then decide between response schema and function calling.
Write the field table and constraints before you write examples.
Add required, enum, or range constraints for every high-risk field.
Define invalid output explicitly and decide whether the system should loud-fail or retry.
Test for semantic correctness, not only parse success.
Saying 'return JSON' without fields, types, or failure rules.
Stuffing machine-consumed outputs with large free-text blurbs.
Using structured output for tasks that really should be tool calls.
Silently swallowing validation failures while bad data keeps moving downstream.
Proof you must keep before launch
One contract spec with explicit fields, types, required values, and constraints.
One schema review outcome showing where downstream guessing would still occur.
One set of examples that proves both success and failure are detectable.
One short recap of why this task should no longer rely on free-form text.
Reusable contract templates
Define fields, types, failure policy, and downstream behavior in one place.
Use it to catch fake structure that still forces downstream systems to guess.
Search Cluster
High-intent users often enter through structured outputs, prompt engineering, or workflow-course searches before they commit to real contract design and review.
Structured Outputs Guide
Many users search for structured outputs because they want JSON-looking responses. DepthPilot cares about something stricter: turning model output into a contract the system can validate, reject, and recover from.
Open pathPrompt Engineering Course
This page targets users who really search for a prompt engineering course, but DepthPilot does not reduce the topic to prompt hacks. It puts prompting back into context architecture, workflow design, and eval loops.
Open pathAI Workflow Course
If the user searches for an AI workflow course, they usually need more than model theory. They need to connect AI into real workflows, tools, access control, and delivery standards.
Open pathReference appendix
These links anchor the method. The actual lesson is the framing order, contract ladder, bad-pattern recognition, and templates above.