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:

1. Authentication — API key in the request header
2. Request — JSON body with model name, messages/prompt, and parameters
3. 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.