Phase 1 · Building with LLMs · Quality & production
Logging and monitoring
See what your LLM app actually did in production.
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Why it matters
When a normal web page breaks, it shows an error. When an LLM feature goes wrong, it usually returns a fluent, wrong answer with status 200, and nobody notices until a customer screenshots it. Logging what every call received, returned, cost and how long it took is how you find those failures, explain them, and feed them back into your evals. It's also how you answer "why did the AI bill double this week?"
What you'll be able to do
- List what to record for every model call and every multi-step run
- Wrap model calls so they're timed and logged even when they fail
- Redact personal data before it reaches your logs
- Turn logs into the handful of numbers worth watching: cost, latency percentiles, errors, truncations
Before this lesson
Next in this phase: Shipping an LLM app