Evaluate generative AI like a product: scorecards, audits, and live tests

Shipping a GenAI feature is easy; proving it's any good is hard. Traditional QA breaks when the output depends on style, tone, or intent.

This post lays out a practical playbook: multi-metric scorecards, structured human reviews, and live experiments tied to real user outcomes. Stop chasing a single number. Build evaluation that matches how people actually use your product.

Why generative AI poses unique quality hurdles

Generative models do not just retrieve facts; they create text, images, and code that can be valid in many ways. Classic benchmarks often miss context and purpose, which is why AI evaluation needs blended signals, not one top-line score. A cross-modal review of methods and pitfalls makes this point clearly across text, vision, and code Evaluating Generative AI.

Subjective tasks rarely have a single correct answer. The same prompt might call for a formal legal tone, a punchy marketing headline, or a playful social caption. No surprise that teams keep debating what “accuracy” even means, as builders in r/GenAI4all have shared from real products defining accuracy for GenAI apps.

Open-ended prompts also do not lend themselves to binary outcomes. Quality often looks like “good enough for purpose,” not perfect. That makes user behavior the real benchmark, a theme emphasized in Statsig’s guide to testing and optimizing AI your users are your best benchmark.

There are hard constraints to respect: nondeterminism, drift, and bias. AI evaluation must measure stability under small input changes and domain shifts. Many organizations still struggle to show impact at all - 85% call impact measurement their top AI challenge in community discussions on r/technology 85% cite impact measurement as the top challenge.

Here is how to start moving in the right direction:

• Use multi-metric scorecards: overlap, fluency, and task success.

• Add structured human review; define rubric and scale.

• Prove value with online experiments; tie metrics to user outcomes introduction to AI experimentation.

Comparing metrics and their constraints

BLEU, ROUGE, and FID measure closeness to references. Useful for summarization, captioning, or parity checks, but creativity often escapes these nets. Perplexity rates fluency, not usefulness. A broad survey catalogs where these metrics help and where they fall short across modalities evaluation methods.

Automated ai evaluation runs fast and scales. It can still miss intent, tone, and task nuance. Chip Huyen has pushed for product-first, user-grounded checks, which pairs nicely with the Statsig guide on user benchmarks AI Engineering with Chip Huyen and user benchmarks.

Benchmarks drift over time, users change, and domains diverge. Cross-domain parity remains elusive for text, image, and code, a gap practitioners often call out in r/artificial discussions implementation challenges. It also explains why so many teams still struggle to measure impact with confidence measure impact.

So treat metrics as guardrails, not judges. Then validate with real traffic and task success through online experiments online experimentation and this practical setup for GenAI apps walkthrough.

A few rules of thumb:

• Define “good” with user actions - avoid proxy-only scores.

• Mix reference metrics with structured reviewer rubrics; capture rationales.

• Stress test for noise, domain shift, and prompt perturbations; watch stability.

Incorporating retrospective evaluations and live feedback

Once the loop is running, start collecting live signals. Quick ratings like thumbs up or down verify alignment with goals. Tie outputs to tasks and success metrics, as outlined in Statsig’s guide guide.

Automation benefits from a second set of eyes. Structured expert audits surface tone, safety, and bias issues that metrics miss. The multi-modal evaluation overview highlights both methods and pitfalls to watch out for overview.

Monitoring should lead to fast fixes. Detect drift; escalate failures to reviewers; patch prompts, retrieval rules, or data. Teams frequently cite accuracy confusion as a core risk in r/GenAI4all threads accuracy challenges.

Close the loop with A/B trials that validate changes under ai evaluation pressure. The Statsig primer shows how to test safely and make decisions without stalling delivery primer. Many orgs still struggle to measure impact, so start simple and add complexity as confidence grows survey.

Practical steps that pay off:

• Capture thumbs up/down; attach task labels and ground truth where possible.

• Route edge cases to experts; store rulings for reuse.

• Audit weekly; refresh test sets to reflect new user intents.

• Compare cohorts; track retention, task success, and wait time.

• Log prompts and outputs; scrub PII; sample for targeted ai evaluation.

Practical strategies for scaling evaluations

Small checks rarely survive production scale. Grow with staged evaluations that gate progress at clear checkpoints and preserve compute. Each gate needs crisp pass criteria - no exceptions. The evaluation frameworks summarized by researchers are a solid starting point Evaluating Generative AI.

A simple sequence works well: offline asserts; sandbox with guardrails; canary; full rollout. Track user outcomes at every stage. This mirrors the Statsig primer on AI experimentation and Chip Huyen’s product-first stance on evaluation Introduction to AI Experimentation and AI Engineering with Chip Huyen.

Add value-based oversight where risk lives. Audit outputs for bias and harmful content before scale. Anchor checks to explicit values and real-world harms documented in both evaluations literature and r/artificial threads Evaluating Generative AI and r/artificial.

Go modular so teams can swap parts without chaos. Standardize prompts, datasets, and judges; version everything. This reduces drift that many report in r/generativeAI conversations key challenges.

Include core modules:

• Deterministic checks: format, policy, and safety.

• Quant metrics: BLEU, ROUGE, FID - used judiciously Evaluating Generative AI.

• User signals: retention and task success, as detailed in Statsig’s guide guide to testing and optimizing AI.

This setup closes the ai evaluation loop and helps chip away at the impact gap many teams still feel 85% challenge.

Closing thoughts

Generative AI needs product-grade evaluation: blended scorecards, structured human judgment, and live experiments tied to user outcomes. Use metrics as guardrails, not judges, then let real behavior decide the winner. Start simple, stress test often, and version everything so progress sticks.

For more, these are worth a read:

• Statsig’s guide to testing and optimizing AI link

• Introduction to AI experimentation from Statsig link

• Evaluating Generative AI: challenges, methods, and future directions link

• Experimenting with generative AI apps in practice link

• AI engineering with Chip Huyen link

Hope you find this useful!