Move beyond “vibes-based” evaluations to systematic, measurable testing that drives continuous product improvement.
By Favour Patrick | Categories: Training
It’s now simple to spin up an AI-native product with Lovable plus a helper like ChatGPT. You talk to users, capture the painful bits, drop the notes into Miro, and let Miro’s clustering sort themes and sentiments. Then sketch a few ways the fix could look, ask ChatGPT to draft a spec, and let Lovable assemble a working prototype. Tech founders have never had an easier time building user-centric, lean, low-cost MVPs.
Since Future Habits founder Kasia Sadowska also runs a startup accelerator with Founder Institute, she hears lines like “My mentor said I need AI in my product to raise money” or “Investors only look at products with AI.” Founders often add AI just to tick a box. If you added model features for the sake of it, let’s help them earn their keep. Track how people actually use them. Which features help, which confuse, and where a human handoff would help.
This post is your path from vibe checks to real evals for Lovable builds.
What evals are
Evals are structured tests for model behavior. Think of them as unit tests and quality gates for your product, tied to outcomes that matter. They answer “Does this meet our bar?” before you ship.
Why now? Rapid prototyping brings probabilistic outputs, shifting model versions, and high user expectations for consistency. Evals define “good,” then check it automatically.
The 3-phase framework
Phase 1: Ground in reality. Study real usage first. Pull about 100 representative traces, label passes and fails, and create a simple list of failure modes through open and axial coding with a single domain decider, the benevolent dictator.
Phase 2: Build reliable evals. Use code checks for objective mistakes and an LLM-as-judge for subjective quality. Validate your judge against human ground truth and measure true positive rate and true negative rate, not just accuracy.
Phase 3: Operationalize. Run evals on every prompt, model, or architecture change, monitor in production, and feed failures back into product work. This creates a continuous improvement loop.
What you measure
Tie model checks to business outcomes so the team cares. Examples include escalation accuracy to reduce support load, helpfulness to lift satisfaction, hallucination rate to protect trust, multilingual accuracy to reach new markets, and resolution rate to reduce churn. Set clear thresholds for each.
Cover five groups of metrics: capability, safety, user experience, reliability, and efficiency. For efficiency, track latency, cost per request, and token use.
Lovable specifics that trip teams up
Lovable apps change quickly and often include several model components. Test at four layers: prompt, component, end-to-end flows, and live monitoring. Add lightweight logging in components, push traces to your eval service, and alert when quality dips.
A minimal pattern looks like this: log traces on the client, trigger a webhook that evaluates a rolling sample, record pass or fail, and notify when fail rates move. If you use LangChain under the hood, LangSmith helps trace chains.
Common traps and fixes
Vibes over evidence. Skip vanity dashboards. Start with error analysis on real traces and connect every metric to user value.
Accuracy theater. Accuracy hides what matters in imbalanced sets. Track true positive and true negative rates and the cost of each mistake.
Uncalibrated judges. Build a small ground-truth set first, then tune your LLM judge and test it on a held-out set.
Stale eval suites. Retire tests that never fail and add new ones as failure modes shift. Review monthly.
Single-method testing. Combine code checks for deterministic errors with LLM judging for tone, clarity, and relevance.
No workflow wiring. Put evals in CI, block merges that fail critical checks, and sample production continuously.
Tools that play nice
Pick a small stack. Choose one from each group to start.
Evals and test harnesses OpenAI Evals with Python templates, DeepEval, promptfoo, Ragas for retrieval checks, TruLens for feedback functions
Tracing and observability LangSmith, Langfuse, OpenTelemetry for LLMs, Arize Phoenix for notebook-style error analysis
Experimentation and feature flags Statsig, LaunchDarkly, or a simple GitHub Actions canary
Data and labeling Label Studio for lightweight labeling, synthetic test data with Mostly AI or Gretel when privacy matters
Regression control and CI GitHub Actions, GitLab CI, or CircleCI to run evals on every pull request, with validators such as Guardrails AI or pydantic
Cost and latency tracking Usage logs from your model provider, plus Evidently AI or WhyLabs for drift and performance checks
Prompt management, optional Weights and Biases Prompts, PromptLayer for history and quick comparisons
At FutureHabits.tech we help teams turn model features into results they can measure. We start with your real user traces, define clear pass and fail rules, and connect evals to your build pipeline. Quality improves with every release, not just every quarter.
What we deliver:
Scale fast without breaking quality. Ship behind flags, run evals on each pull request, and catch regressions before users do.
Keep costs in check. Track latency, token use, and hit rate for each feature. Remove or rethink anything that does not move a clear outcome.
Ready to align your product with clear metrics and a lean cost profile? Get in touch. We will make your AI features measurable, your releases safer, and your cost per outcome healthier so you can scale with pace and focus.
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