Lilly Tech Systems
Learn API Design for AI Products
Design and build production-grade APIs for AI-powered applications — from REST and GraphQL endpoints to real-time streaming interfaces that serve ML model predictions at scale.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
Why API design matters for AI products, key challenges, and how AI APIs differ from traditional APIs.
2. REST API Design
Design RESTful APIs for ML model serving: endpoints, request/response schemas, versioning, and error handling.
3. GraphQL
Build flexible AI APIs with GraphQL: schemas, resolvers, subscriptions, and when to choose GraphQL over REST.
4. Streaming APIs
Implement streaming responses for LLMs and real-time AI: SSE, WebSockets, and gRPC streaming patterns.
5. Documentation
Document AI APIs effectively: OpenAPI specs, interactive docs, SDKs, and developer experience best practices.
6. Best Practices
Rate limiting, authentication, monitoring, versioning strategies, and scaling AI APIs in production.
What You'll Learn
By the end of this course, you'll be able to:
Design AI-First APIs
Create intuitive API interfaces that handle the unique challenges of ML model serving and AI workloads.
Build Streaming Endpoints
Implement real-time streaming for LLM outputs using SSE, WebSockets, and gRPC streaming.
Document for Developers
Write API documentation that developers love, with interactive examples and auto-generated SDKs.
Scale for Production
Apply rate limiting, caching, versioning, and monitoring strategies for production AI APIs.