Offline vs online evals: Choosing evaluation timing

Shipping AI without a real evaluation plan is a fast way to burn time, budget, and trust. A model can look great in a notebook, then fall apart once it meets production traffic and real users.

Here’s the fix: treat evals like a product delivery muscle, not a one-off task. Start offline, graduate online, then loop. The result is simple and powerful: ship with confidence, not vibes.

Identifying the right moment for evaluation

Start with offline AI evals the moment anything meaningful changes. New model, big prompt rewrite, or a fresh feature in the ranking stack. Lock a dataset, define a clear grader, and anchor decisions on a fixed setup. Statsig’s docs lay out the basics well: offline evals.

When offline gains look steady, switch to online AI evals. Shadow the current live version, score both silently, and compare grades across traffic before exposing users. The mechanics are straightforward with Statsig’s online evals.

Use crisp triggers to avoid guesswork:

• A new model or major prompt rewrite

• New signals or features in ranking

• Cost, latency, or safety targets at risk

Do not wait for perfection. Move from sandbox to production when gaps narrow, not vanish. Start with small flights, then scale as wins persist. The case for this measured rollout is covered in Statsig’s writeup on online experimentation.

Check infra health before any online checks. Run load tests using ApacheBench, following Martin Kleppmann’s walkthrough of practical pitfalls and setup tips: ApacheBench. Add tracing and monitoring on key paths. Many teams share what actually works in production in these r/LLMDevs threads on evals and tracing and on monitoring live models: approaches to eval and tracing and prod monitoring.

Leveraging offline evals for early alignment

Offline evals with historical data are where early alignment happens. They are fast, low risk, and great for validating progress without touching live users. The goal is simple: prove the direction is right before spending real traffic. Details and examples live in Statsig’s guide to offline evals.

Pick metrics that match the problem. For recommenders, track precision, recall, MAP, and NDCG. For LLMs, score hallucination, tone, and correctness. Lenny Rachitsky’s PM guide is a practical reference for choosing these criteria and keeping them honest: evals for PMs.

Guard against bias that inflates offline wins. Exposure bias is the usual suspect. In practice:

• Use IPS or counterfactual checks to adjust for logged policy exposure

• Add diversity and novelty metrics so value isn’t just repetition of yesterday’s hits

A simple plan that holds up under pressure:

1. Define prompts, datasets, and graders, then run evals in a stable harness: offline evals and the broader ai evals overview.

2. Align offline metrics with live goals, then stage online checks next: online evals.

3. Learn from peers and tools: eval and tracing tactics, production monitoring, and open-source evaluators all show up in these threads: approaches to eval and tracing, prod monitoring, and frameworks.

Offline checks make iteration cheap and fast. They fit a test-ship-learn loop that Statsig argues is essential for AI products: AI experimentation.

Refining with online evals in real-world settings

Once offline signals stabilize, head to online evals. Real users will expose gaps that curated datasets miss. Keep continuity by comparing live grades against the offline baseline. Statsig’s online evals make the shadow-and-compare step straightforward.

Roll out safely. A/B tests, interleaving, and shadow mode keep risk contained. Score each output with LLM-as-a-judge to get consistent quality grades, then track business impact separately. The concept and tradeoffs are summarized in the ai evals overview. Avoid single-metric myopia. Durability and retention matter more than a one-day spike.

Here is a tight live checklist:

• Define a metric suite across quality, latency, and cost, taking cues from this PM-focused guide: eval criteria.

• Route a small slice of traffic to candidates while most users stay on the stable default.

• Record grades and traces, then compare cohorts, not just aggregates. Teams share practical dashboards and alerts in this thread: prod monitoring.

• Investigate gaps between offline and online. Fix feature mismatches first, then revisit prompts and graders.

Close the loop in production. Fold AI evals into dashboards and alerting, and stress-test under load to avoid false positives. The ApacheBench example from Martin Kleppmann is a battle-tested starting point: load test example. For teams using Statsig, pairing online evals with feature flags and experiments keeps rollouts clean and reversible.

Balancing both approaches for greater success

The best teams alternate offline and online work. That rhythm merges speed with real-world signal. It keeps the eval loop tight, honest, and repeatable.

Give each lane a clear job. Offline evals answer correctness and bias questions using stable datasets and graders, guided by Statsig’s offline evals and the PM-focused rubric in Lenny’s guide. Online evals answer impact and engagement questions with cautious traffic, as covered in online evals.

Targets that keep teams aligned:

• Offline: accuracy, hallucination rate, and grader agreement

• Online: conversion, retention, latency, and cost per task

Back it with a steady experimentation cadence. Statsig’s take on why AI products need this discipline is a useful reference: AI experimentation.

Close the loop with traces and feedback in production. Capture real traffic, grade outputs, and learn. The r/LLMDevs community has solid examples for both monitoring and eval-plus-tracing workflows: LLM monitoring and evals and tracing. Use open-source evaluators where they fit: frameworks. Version prompts and graders in your delivery pipeline, as covered in the ai evals overview. And keep running capacity and tail-latency tests with ApacheBench to stay honest under load: ApacheBench guidance.

Closing thoughts

The playbook is short: start offline, graduate online, then iterate with discipline. Use small flights, track quality and business impact separately, and never skip the infra checks. Keep prompts, graders, and datasets versioned so changes are intentional. Statsig’s AI Evals and experimentation tools make this workflow easier to operate and audit end to end.

More to dive into:

• Statsig docs: offline evals, online evals, and the ai evals overview

• Why AI products need experimentation: online experimentation

• Metrics and practical criteria: Lenny’s PM guide

• Load testing example: ApacheBench

• Community discussion and tools: approaches to eval and tracing, prod monitoring, and frameworks

Hope you find this useful!