Calling AI APIs: A Practical Guide

This one's for the dev team — but anyone curious about how AI gets built into real products, stick around. The leap from "using ChatGPT in a browser" to "AI integrated into your app" is smaller than you think. Let me walk you through it.

The basics in 60 seconds: Every major AI provider (Anthropic/Claude, OpenAI/GPT, Google/Gemini) offers an API — a way for your code to send text to the AI and get text back. You send a message, you get a response. It's an HTTP request, just like any other API your app already calls.

Getting started with Claude's API:
```
1. Get an API key from console.anthropic.com
2. Install the SDK: pip install anthropic (Python) or npm install @anthropic-ai/sdk (Node)
3. Make your first call: send a message, get a response
4. That's it. You're now building with AI.
```

Three things that trip people up:
• Rate limits. APIs have usage limits. For prototyping this doesn't matter. For production, you need to handle rate limit errors gracefully (retry with backoff).
• Streaming. For a good user experience, stream the response token by token instead of waiting for the whole thing. Every SDK supports this — it's a flag, not a rewrite.
• Cost. API calls cost money per token (roughly per word). Claude Haiku is cheap enough for most use cases. Track your usage from day one so there are no surprises.

What to build first: Start with something internal. A Slack bot that answers team questions from your docs. A script that summarizes your daily support tickets. A tool that drafts release notes from your git history. Internal tools have lower stakes and give your team real API experience.