Intermediate~8 hours6 lessons

Structured Outputs & AI API Design

Build reliable AI APIs that power real products: JSON mode, Instructor/Pydantic integration, streaming, retry patterns, rate limiting, and OpenAI-compatible endpoint design.

What you'll build: a schema-validated JSON data extraction pipeline

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Curriculum

Module A: Structured Generation

3 lessons

Lesson 1: JSON Mode, Instructor & Pydantic Integration

Force LLMs to output valid, schema-validated JSON every time using Anthropic's tool-use JSON mode and the Instructor library. Define Pydantic models as your output schema and get type-safe responses.

75 min

Lesson 2: Streaming LLM Responses

Implement real-time token streaming in FastAPI using Server-Sent Events (SSE). Handle streaming structured output, partial JSON validation, and client-side rendering of incremental responses.

70 min

Lesson 3: Retry Patterns & Validation Loops

Build self-healing LLM calls that automatically retry on schema validation failures, parse errors, or low-confidence outputs. Implement exponential backoff and max-retry budgets that degrade gracefully.

60 min

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Force LLMs to return valid JSON using schemas, Pydantic models, and function calling.

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Module B: API Design & Production

3 lessons

Lesson 4: Rate Limiting, Quotas & Multi-Tenancy

Protect your AI API from runaway costs and abuse: implement per-user token quotas, sliding-window rate limiting with Redis, and fair queuing for concurrent multi-tenant workloads.

65 min

Lesson 5: Building OpenAI-Compatible APIs

Design a drop-in OpenAI API endpoint that routes to Anthropic or any open-source model, adding semantic caching, model routing, and usage tracking — enabling client code to switch providers transparently.

70 min

Lesson 6: Capstone — Build a Production AI API

Build a production-grade AI extraction API: Pydantic output schemas, streaming responses, retry-on-validation-failure loop, per-user rate limiting, cost tracking, and an OpenAI-compatible interface.

120 min

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Design production APIs that combine structured LLM outputs with validation and retry logic.

Fireship9:18

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