Build a Practical Tools and Structured Outputs Example in AI Engineering

Define the build target

For Tools and Structured Outputs, build a small AI workflow with a fixed evaluation set and record output quality, failure cases, latency, and cost-related observations. Build the boundary case using this implementation lens: Use model/provider interfaces, prompts and context windows, retrieval, tool calls, structured outputs, evaluation datasets, safety/privacy controls, latency/cost telemetry, and failure recovery.

Keep the Tools and Structured Outputs build centered on these technical constraints: Input/context contract. Model/provider abstraction. Apply them through this path lens: Use model/provider interfaces, prompts and context windows, retrieval, tool calls, structured outputs, evaluation datasets, safety/privacy controls, latency/cost telemetry, and failure recovery. Use model/provider interfaces, prompts and context windows, retrieval, tool calls, structured outputs, evaluation datasets, safety/privacy controls, latency/cost telemetry, and failure recovery.

Implement the core behavior

Implement Tools and Structured Outputs around the module artifact—a working tools and structured outputs example with an explicit success and failure check—and keep the implementation specific to this path context: Use model/provider interfaces, prompts and context windows, retrieval, tool calls, structured outputs, evaluation datasets, safety/privacy controls, latency/cost telemetry, and failure recovery.

from pydantic import BaseModel

class Answer(BaseModel):
    answer: str
    source_ids: list[str]

def validate_result(raw: dict, allowed_sources: set[str]) -> Answer:
    result = Answer.model_validate(raw)
    if not set(result.source_ids) <= allowed_sources:
        raise ValueError('response cited an unknown source')
    return result

python3 -m pytest -q

Structured output is schema-validated and cannot cite source IDs outside the retrieved context.

practice/\n├── README.md\n├── tools-and-structured-outputs-build.py\n└── evidence/\n    └── expected-result.txt

Run the complete path

Run one realistic Tools and Structured Outputs case end to end and record the required evidence: the relevant output, test, log, query result, or rendered state for Tools and Structured Outputs. Interpret the result through this path context: Use model/provider interfaces, prompts and context windows, retrieval, tool calls, structured outputs, evaluation datasets, safety/privacy controls, latency/cost telemetry, and failure recovery.

Change one meaningful condition

Modify one condition central to Tools and Structured Outputs using this path context: Use model/provider interfaces, prompts and context windows, retrieval, tool calls, structured outputs, evaluation datasets, safety/privacy controls, latency/cost telemetry, and failure recovery. Predict the new result before rerunning the same workflow.

Verify the artifact

Your deliverable is a working tools and structured outputs example with an explicit success and failure check.

Common mistakes to avoid

• Prompt changes not versioned.
• Retrieval returns irrelevant context.
• Structured output not validated.
• No test set for regressions.