I’ve been shipping GenAI features long enough to know that clever prompts and orchestration aren’t enough. What actually matters is evidence: Does the system work, for whom, and under what conditions? That’s where rigorous AI evals come in—the backbone of building reliable, safe, and continuously improving AI products.
In a recent conversation focused entirely on evaluation, I dug into what “evals” mean in the AI/ML world, why they’re more than just quality assurance, and how to operationalize them end to end. If you want to explore the discussion, listen on Spotify: https://open.spotify.com/episode/7mSiEGSYNO4sXeGAVTJO4V or Apple Podcasts: https://podcasts.apple.com/kh/podcast/ai-evals-discovery/id1794203808?i=1000727980774. There’s also a video version on YouTube: https://www.youtube.com/watch?v=pfSIQMrWhQE.
Here’s how I frame evals with my teams. First, define the behavior you want to see in terms real users care about. Then codify that intent as tests that run consistently. I distinguish between golden datasets, synthetic data, and real-world traces. Golden datasets capture canonical examples that represent “ground truth.” Synthetic data fills important gaps quickly and safely. Real-world traces keep you honest and reflect evolving usage.
The most durable loop I’ve found is simple: identify error modes, turn them into evals, and automate. This is where error analysis pays off. Some checks should be purely deterministic—code-based checks that evaluate structured outputs, schemas, or policies. Others benefit from LLM-as-judge when human-like judgment matters, as long as you calibrate and continuously verify those judges with spot checks and inter-rater agreement.
Discovery practices should inform every evaluation step. If you’re doing “Story-Based Customer Interviews,” you can derive realistic scenarios, acceptance criteria, and edge cases directly from user narratives. That context sharpens the evals and prevents you from overfitting to toy problems or proxy metrics that don’t reflect user value.
Evals require ongoing care and feeding. Criteria drift is real—what counted as “good” six weeks ago may not satisfy users after you ship a new capability or your audience evolves. I treat the eval suite like living product infrastructure: versioned, reviewed, and owned. When we change prompts, models, or retrieval strategies, the evals run first, then we examine deltas, regressions, and surprises before anything reaches production.
Guardrails and human oversight work hand-in-hand with evals. Guardrails enforce non-negotiables (safety, privacy, compliance), while evals measure progress against nuanced goals (relevance, helpfulness, tone). In high-stakes workflows, I combine pre-deployment evals, runtime guardrails, and spot human review. The goal isn’t to eliminate humans; it’s to focus their attention where judgment and context matter most.
Practically, I start with a minimal eval harness that standardizes inputs and outputs—often in JSON (JavaScript Object Notation)—and writes repeatable tests. I maintain a small golden dataset, add targeted synthetic data for coverage, and stream real-world traces into the suite once we have consent and redaction in place. For subjective criteria (e.g., tone, helpfulness), I layer in LLM-as-judge with calibration. For objective checks (e.g., schema validation, policy compliance), code-based checks are my default.
Tooling evolves quickly, but the principles hold. Whether you’re working with Anthropic or experimenting with V0 or Lovable in your prototyping stack, the eval loop stays the same: define success, test it the same way every time, and close the loop with learning. If you’re a product creator or leading forward deployed engineers, this discipline accelerates gen ai for product prototyping without sacrificing safety or quality.
I also tie evals to outcomes vs output OKRs. Instead of “ship three prompts,” we commit to measurable outcomes like resolution rate, time-to-answer, or a target “helpfulness” score. In customer support ai strategy, we monitor real-world traces, CSAT, and handoff quality to ensure the AI augments agents rather than creating silent failure modes. That’s how evals drive product-market fit lessons instead of just dashboards.
If you want to go deeper, explore these foundational concepts and tools: ML (Machine learning), LLM (Large language model), “AI Evals for Engineers and PMs”: https://maven.com/parlance-labs/evals, “The Product Leadership Wheel – A Framework for Defining and Growing Product Leadership at Scale”: https://www.petra-wille.com/plwheel, “How I Designed & Implemented Evals for Product Talk’s Interview Coach”: https://www.producttalk.org/2025/09/interview-coach-evals/, “Behind the Scenes: Building the Product Talk Interview Coach”: https://www.producttalk.org/2025/08/customer-interview-coach/, V0: https://vercel.com/docs/v0, JSON (JavaScript Object Notation): https://en.wikipedia.org/wiki/JSON, Anthropic: https://www.anthropic.com/, Lovable: https://lovable.dev/, and “Story-Based Customer Interviews”: https://learn.producttalk.org/course/story-based-customer-interviews.
If this resonates, I’ll be sharing weekly lessons learned from building and evaluating AI features in the wild, plus conversations with cross-functional teams about real-world AI development. Have thoughts or a tactic that’s worked for you? Drop a comment and let’s compare notes.
Inspired by this post on Product Talk.
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