Phase 1 · Building with LLMs · Quality & production
Shipping an LLM app
Deploy, secure keys, rate-limit and handle failures.
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Why it matters
A script on your laptop becomes a product the moment other people can use it, and every one of them can now spend your money. Shipping an LLM app is mostly ordinary web engineering (keep secrets on the server, check inputs, limit abuse, handle failure) applied to a dependency that's slow, metered and occasionally overloaded. Get these right and a small team can run an AI feature safely; skip them and a single leaked key or a looping script can run up a four-figure bill overnight.
What you'll be able to do
- Put the model call behind your own backend so the API key never reaches a browser
- Protect the endpoint with input limits, per-user rate limits and spending caps
- Fail gracefully: friendly errors, no leaked internals, a fallback when the model is busy
- Walk through a launch checklist for a small LLM feature
Before this lesson
Next in this phase: Capstone: research agent