Intermediate

Documenting AI APIs

Create developer-friendly API documentation with OpenAPI specifications, interactive playgrounds, SDK generation, and best practices for AI-specific docs.

Why Documentation Is Critical

For AI APIs, documentation serves as both a user manual and a trust-building tool. Developers need to understand not just how to call your API, but what the AI model does, its limitations, and how to get the best results.

OpenAPI Specification

Define your AI API using OpenAPI (formerly Swagger) for auto-generated docs and SDK generation:

openapi: 3.1.0
info:
  title: AI Prediction API
  version: 1.0.0
  description: Production ML model serving API

paths:
  /v1/predict:
    post:
      summary: Generate a prediction
      operationId: createPrediction
      requestBody:
        required: true
        content:
          application/json:
            schema:
              $ref: '#/components/schemas/PredictionRequest'
            example:
              model: "sentiment-v2"
              text: "This product is amazing!"
              temperature: 0.0
      responses:
        '200':
          description: Successful prediction
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/PredictionResponse'
        '429':
          description: Rate limit exceeded
          headers:
            Retry-After:
              schema:
                type: integer

AI-Specific Documentation Needs

Model Cards

Document model capabilities, limitations, training data, bias considerations, and intended use cases for each available model.

Prompt Guides

Provide prompting best practices, example prompts for common tasks, and tips for getting optimal results from the model.

Rate Limit Details

Clearly document rate limits per tier, token limits, concurrent request limits, and how to handle 429 responses.

Pricing Calculator

Interactive pricing calculator showing cost per token, per request, and estimated monthly costs for typical usage patterns.

Interactive API Playground

Let developers test your API directly from the documentation. Key features to include:

  • Pre-filled examples: Show working requests for common use cases that developers can modify and run.
  • Response preview: Display formatted responses with syntax highlighting and field explanations.
  • Streaming demo: Show real-time token streaming for LLM endpoints so developers see the experience.
  • Error simulation: Let developers trigger common errors (rate limit, invalid input) to test their error handling.
  • Code generation: Auto-generate request code in Python, JavaScript, curl, and other languages.

SDK and Client Library Design

# Python SDK example - clean, intuitive interface
from myai import Client

client = Client(api_key="sk-...")

# Simple prediction
result = client.predict(
    model="sentiment-v2",
    text="This product is excellent!"
)
print(result.prediction)  # "positive"
print(result.confidence)  # 0.97

# Streaming
for chunk in client.chat.stream(
    model="chat-v1",
    messages=[{"role": "user", "content": "Hello!"}]
):
    print(chunk.text, end="", flush=True)
✅
Generate SDKs from your OpenAPI spec: Use tools like openapi-generator, Stainless, or Fern to auto-generate type-safe client libraries in multiple languages. This ensures SDKs stay in sync with your API and reduces maintenance burden.

Documentation Tools

ToolTypeBest For
MintlifyDocs platformBeautiful API docs with AI search (used by Anthropic)
ReadMeDocs platformInteractive API explorer with analytics
Swagger UIOpen sourceAuto-generated interactive docs from OpenAPI
RedocOpen sourceClean three-panel API reference docs
StainlessSDK generatorType-safe SDKs from OpenAPI (used by OpenAI)