Observability for AI apps: what to log so you're not flying blind
If you have AI in the flow, you need to log prompts, tool calls and decisions (with privacy).
Team
Editorial team focused on development, SaaS and indie devs.
AI apps fail differently: it's not just "error 500", it's wrong decision, wrong tool, bad context. So recent guides emphasize treating AI as a component that needs monitoring and governance.
Why AI apps break differently
The error can be in the prompt, the model, the tool called or post-processing. Without visibility, you don't know where to intervene.
What to log
Execution ID and prompt version. "Clean" inputs (no PII). Tool calls (name, time, success). Final output and confidence score (when available). Fallback reason (alternative model, rule, human).
Privacy and retention
Mask sensitive data. Set short retention. Allow opt-out when applicable. Without observability, you don't improve. You just "hope".
Key takeaways
Log execution, prompts (no PII), tool calls and decisions. Respect privacy and retention.
Read also
- Bilingual (EN/ES) with AI: how to internationalize product without duplicating work
- Dev career with AI: how to increase your value without becoming a prompt pusher
FAQ
How long to retain? The minimum to debug and improve. Days or weeks; not years. Document in the privacy policy.
What about storage cost? Aggregated logs and sampling reduce cost. Prioritize errors and edge cases.
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Fale comigoDisclaimer: This content is for informational purposes only. Consult official documentation and professionals when needed.