LLM API Tutorial
LLM APIs are the production doorway to AI models. Your app sends messages and settings, receives model output, and handles cost, latency, streaming, and failures.
What it is
An LLM API lets your backend call a hosted model with messages, system instructions, tool definitions, and output settings.
Why it matters
Almost every Gen AI app starts by calling a model API reliably and safely from server-side code.
How it works
Send a request with messages and parameters, handle the response, stream chunks when user-facing, and log usage for cost and debugging.
Common mistakes
- Putting API keys in browser code.
- Ignoring rate limits and timeouts.
- Forgetting that APIs are stateless unless you resend history.
Best practices
- Keep provider calls server-side.
- Add retries with backoff.
- Track token usage from day one.
Practical Workflow
client.messages.create(model='...', messages=[{'role':'user','content':'Explain RAG'}])FAQ
Should LLM API calls happen in the frontend?
No. Keep provider keys and model calls behind a protected backend.
When should I stream responses?
Stream long user-facing answers so the interface feels responsive.
Build It In The Roadmap
Turn this tutorial into a focused roadmap step with adjacent concepts and practice prompts.
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