AI APIs let you add powerful capabilities — text generation, image creation, speech recognition, and more — to your applications with simple HTTP requests. This course covers the practical knowledge you need to build with them.
What Is an AI API?
An AI API is a web service that gives you access to AI models without needing to train, host, or manage them yourself. You send a request (e.g., a text prompt), and get back a response (e.g., generated text).
Major Providers
- OpenAI — GPT-5, GPT-4o, DALL-E 3, Whisper, TTS. The most widely used AI API.
- Anthropic — Claude 4, Claude 4 Sonnet. Known for safety, long context windows, and strong instruction-following.
- Google — Gemini 2.5 Pro, Gemini 2.5 Flash. Deep integration with Google Cloud and multimodal capabilities.
- Open Source — Llama 3.1 (Meta), Mistral Large, Mixtral. Self-host or use via providers like Together AI, Fireworks, or Groq.
Common API Patterns
Most AI APIs follow a similar pattern:
- Authentication — API key in the request header
- Request — JSON body with model name, messages/prompt, and parameters
- Response — JSON with generated content, usage statistics, and metadata
``` POST /v1/chat/completions Authorization: Bearer sk-... Content-Type: application/json
{ "model": "gpt-5", "messages": [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Explain APIs in one paragraph."} ], "temperature": 0.7 } ```
Key Concepts
- Tokens — AI models process text in chunks called tokens (~4 characters each). You pay per token.
- Context Window — Maximum tokens a model can process in one request (input + output combined).
- Temperature — Controls randomness: 0 = deterministic, 1 = creative.
- Streaming — Receive the response word-by-word as it generates, rather than waiting for completion.