FastAPI Request and Response Contracts with Pydantic

Fri, 21 Aug 2026 00:00:00 +0000

Whiteboard: FastAPI — FastAPI Request and Response Contracts with Pydantic.

AI endpoints receive untrusted client input and often relay unstable model or third-party output. Pydantic schemas create the first boundary by turning data that looks right into data that has been validated.

Core mental model

Request models validate inputs, domain models enforce business invariants, and response models define the public surface. Do not return ORM objects or raw provider JSON directly.

Key mechanics

Bound string lengths, use Literal for enumerations, and model nested structures. Parse model output before returning it; route failures through an observable 502/422 branch.

Python example

from typing import Literal
from pydantic import BaseModel, Field

class ChatRequest(BaseModel):
 message: str = Field(min_length=1, max_length=4000)
 mode: Literal["answer", "summarize"] = "answer"

class ChatResponse(BaseModel):
 request_id: str
 answer: str
 citations: list[str] = Field(default_factory=list)

Course focus

This article turns the whiteboard into explicit engineering boundaries: define inputs and outputs first, then decide how state, failures, and observability work. The examples use Python and focus on durable design principles rather than a particular provider version; verify APIs against the documentation for your installed dependencies.

Engineering practice

Keep business rules in the application layer instead of hiding them in untestable prompts or route handlers.
Add timeouts, bounded retries, and idempotency keys to external calls; retries are not a complete error strategy.
Record a request id, latency, input version, model/index version, and outcome without logging sensitive raw content.
Use a small fixed regression set first, then monitor quality and cost with sampled production traffic.

Common mistakes

Drawing only the happy path and omitting timeouts, empty results, rate limits, and rollback paths.
Letting one function parse input, call providers, build prompts, and persist data.
Replacing typed contracts with string conventions that can only be verified by manual integration.

Production checklist

Inputs, outputs, and error responses have explicit schemas
External dependencies have timeouts, bounded retries, rate limits, and fallbacks
Logs, metrics, and traces can be correlated to one request
Critical paths have unit tests, integration tests, and offline evaluation samples
Secrets, user content, and provider responses follow least-privilege and privacy rules

Practice

Implement the smallest loop shown on the whiteboard. Inject a timeout, an empty result, and a malformed payload, then check whether the system remains stable and diagnosable. Add one metric that proves your optimization improved quality or latency.

Hands-on exercise

Add request_id, length limits, a mode enum, and citations to a Q&A endpoint. Submit an empty string, oversized text, unknown mode, and missing fields and verify stable client errors.

Conclusion

An AI feature becomes maintainable when every arrow on the whiteboard maps to an input, an output, and a failure strategy.

OpenAI Structured Outputs: Make Model Results Verifiable

Fri, 21 Aug 2026 00:00:00 +0000

Whiteboard: OpenAI — OpenAI Structured Outputs.

Free text is useful for display; structured output is useful for programs. Structured Outputs do not make a model infallible—they make success, refusal, and failure distinguishable to code.

Core mental model

A schema constrains types, required fields, and enums but cannot guarantee factual correctness. Business validation must still check ranges, permissions, and citation existence.

Key mechanics

Separate transport, schema, and domain validation. When the model refuses or returns an incomplete result, do not silently fill defaults and disguise uncertainty as fact.

Python example

from pydantic import BaseModel, Field

class Incident(BaseModel):
 severity: str = Field(pattern="^(low|medium|high)$")
 summary: str = Field(min_length=1, max_length=500)
 actions: list[str] = Field(min_length=1, max_length=5)

response = client.responses.parse(model="gpt-4o-mini", input="Turn this incident log into a structured summary", text_format=Incident)
if response.output_parsed is None: raise ValueError("model_refusal_or_parse_failure")

Course focus

Engineering practice

Keep business rules in the application layer instead of hiding them in untestable prompts or route handlers.
Add timeouts, bounded retries, and idempotency keys to external calls; retries are not a complete error strategy.
Record a request id, latency, input version, model/index version, and outcome without logging sensitive raw content.
Use a small fixed regression set first, then monitor quality and cost with sampled production traffic.

Common mistakes

Drawing only the happy path and omitting timeouts, empty results, rate limits, and rollback paths.
Letting one function parse input, call providers, build prompts, and persist data.
Replacing typed contracts with string conventions that can only be verified by manual integration.

Production checklist

Inputs, outputs, and error responses have explicit schemas
External dependencies have timeouts, bounded retries, rate limits, and fallbacks
Logs, metrics, and traces can be correlated to one request
Critical paths have unit tests, integration tests, and offline evaluation samples
Secrets, user content, and provider responses follow least-privilege and privacy rules

Practice

Hands-on exercise

Add a schema version and backward-compatible fields. Prepare normal, refusal, missing-field, and out-of-range fixtures and verify the API never returns half-valid JSON as success.

Conclusion

An AI feature becomes maintainable when every arrow on the whiteboard maps to an input, an output, and a failure strategy.

Pydantic | 1byteone - Java Backend / AI Application Developer

FastAPI Request and Response Contracts with Pydantic

Core mental model

Key mechanics

Python example

Course focus

Engineering practice

Common mistakes

Production checklist

Practice

Hands-on exercise

Conclusion

OpenAI Structured Outputs: Make Model Results Verifiable

Core mental model

Key mechanics

Python example

Course focus

Engineering practice

Common mistakes

Production checklist

Practice

Hands-on exercise

Conclusion