What is the difference between offline and online evaluation?

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Offline evaluation runs your system against a fixed dataset before you ship. Online evaluation measures the live system on real user traffic after you ship.

Offline gives a fast, repeatable signal with known expected answers, so you can compare two prompts fairly. But a fixed dataset never covers everything real users ask.

Online sees the real mix of user inputs, but usually has no ground truth. You lean on judges, user signals, and product outcomes instead.

	Offline eval	Online eval
When	before release	after release, continuously
Data	curated, fixed set	live production traffic
Ground truth	usually available	usually missing
Blocks a deploy	yes	no, it alerts or triggers rollback

They form a loop, not a choice. Ship on offline results, watch online, then feed the failures you find back into the offline set.

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Likely follow-ups

Offline scores improved after a change, but production quality dropped. How would you debug that? Compare the eval set's input mix with live traffic, read traces of failing cases, and ask whether you overfit the set or the metric misses what users want.
How do failures you find in production get back into your offline eval set? Sample them from traces and user signals, have someone label the expected behavior, and add them to a versioned set as regression cases.
Online you rarely have ground truth. Which signals do you rely on, and where does each one mislead? Cover LLM judges on sampled traffic, thumbs and implicit signals like retries, and product outcomes, plus the bias or noise each one carries.
Where do shadow deployments and canary releases fit between offline and online eval? Shadow runs the new version on real inputs without showing its output to users. Canary gives a small slice of users the change, with rollback ready.
How would you A/B test a prompt change, and what makes it harder than a normal A/B test? Randomize by user, pick a product outcome as the main metric, watch latency and cost too, and expect noisy quality signals to need bigger samples.

What you can say

Offline eval runs your system against a fixed dataset before you ship. Online eval measures the live system on real user traffic after you ship.
Offline is fast and repeatable, and you usually know the expected answers, so you can compare two prompts fairly and use it to block a deploy.
Its weakness is coverage, because a fixed dataset never covers everything real users ask.
Online sees the real mix of inputs, but it usually has no ground truth, so I lean on LLM judges, user signals and product outcomes instead.
It runs continuously after release and doesn't block a deploy. It alerts or triggers a rollback.
Neither is enough on its own, so it's a loop, not a choice. I ship on offline results, watch online, and feed the failures I find back into the offline set.

Weak answers to avoid

Treats them as either-or Picking one misses the point. Say offline gates the release, online catches what the dataset missed, and those failures go back into the offline set.
Assumes online eval has ground truth Live traffic rarely comes with correct answers. Name what you use instead, such as LLM judges, user signals like thumbs, and product outcomes.
Treats a strong offline score as proof A fixed dataset never covers every real query, so a good offline number can hide failures. Explain that online monitoring is there to catch them.
Only says before vs after release Timing is the easy part. Also cover the data each one sees, whether ground truth exists, and that offline blocks a deploy while online alerts or rolls back.

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