Do I need logs and evals for production LLM apps?

Yes — for production LLM apps, you generally want both logs and evals.

Why logs matter

Logs help you:

• debug bad outputs
• trace failures end-to-end
• monitor latency, cost, token usage, and error rates
• understand what prompts/context were used when something went wrong
• audit behavior for compliance or safety

For LLM apps, logging is especially useful because issues are often non-deterministic and prompt-dependent.

Why evals matter

Evals help you:

• measure quality systematically
• catch regressions when prompts/models/tools change
• compare models or prompt versions
• validate safety, accuracy, groundedness, and task success
• create release gates before pushing changes

Without evals, you’re mostly relying on anecdotal testing, which is risky in production.

Best practice

Use them together:

• Logs tell you what happened.
• Evals tell you whether it was good.

Practical recommendation

At minimum, track:

• request ID / trace ID
• prompt version
• model version
• tool calls
• retrieved documents / context
• output
• latency
• token counts / cost
• user feedback

And run evals on:

• a fixed golden set
• recent production samples
• edge cases and failure modes
• any prompt/model change before deployment

Short answer

If your LLM app matters to users or your business, then yes, you should have logs and evals.

If you want, I can also give you a minimal production observability stack for LLM apps.