AI Evaluation for Product Managers: From Vibe Coding to Production Confidence

Product managers shipping AI features face a fundamental problem. You look at an LLM output and think “this seems fine” without any systematic way to validate that judgment. OpenAI’s CPO and Anthropic’s CPO both publicly state their models hallucinate. The people selling you the product tell you not to trust it blindly.

Evaluation systems close the gap between “looks good to me” and “we have data proving this works.” This guide covers building your first LLM-as-judge eval, scaling from single examples to data sets, and validating that your eval itself works. Total implementation time: one afternoon. Ongoing confidence: permanent.

TL;DR

AI evaluation replaces gut-check reviews with systematic testing. You write prompts that judge LLM outputs, run them against data sets of real examples, and compare LLM judgments to human labels. When the two align, you ship with confidence. When they diverge, you iterate. The workflow takes hours to set up and saves weeks of production debugging.

Key Takeaways

• Vibe coding works for prototypes but fails at production scale
• LLM-as-judge evals classify outputs using text labels, not numeric scores
• Data sets of 10-100 examples catch issues that single-row reviews miss
• Human labels validate your eval system before you trust it
• Eval prompts need iteration just like product prompts
• The PM who controls the eval controls the product outcome
• Eval as acceptance criteria replaces PRDs for AI features

What Is an AI Evaluation?

An AI evaluation is a prompt that classifies LLM output against defined criteria. You tell a judge model what to look for, give it the text to evaluate, and receive a label. The label maps to pass/fail decisions in your workflow.

The eval prompt contains four components:

• Role: Define what the judge model does
• Context: Provide the text being evaluated
• Goal: Specify what the judge determines
• Labels: Give the possible outputs (toxic/not toxic, friendly/robotic)

LLMs perform poorly when asked to generate numeric scores directly. Tokens represent text patterns, not mathematical values. Ask for text labels instead. Map those labels to scores in your code if your systems require numbers.

Action item: Write your first eval prompt. Pick one quality dimension (helpfulness, accuracy, tone) and define two labels (good/bad, meets criteria/fails criteria).

Why Does Vibe Coding Fail at Scale?

Vibe coding means looking at one LLM output and deciding it seems acceptable. This works when you build a weekend prototype. It breaks when you ship to hundreds or thousands of users.

The core problem is coverage. One example tells you nothing about edge cases. Your agent might handle standard queries perfectly while failing on unusual inputs. Without systematic evaluation, you discover these failures in production through user complaints.

The secondary problem is consistency. Different team members apply different standards. The same person applies different standards on different days. No baseline exists for what “good enough” means.

Eval systems solve both problems. You define explicit criteria. You run against representative data sets. You catch failures before users do.

Action item: List three recent AI feature reviews where you approved based on gut feel. Note what criteria you implicitly applied. Write those criteria as eval prompt components.

How Do You Build Your First Eval?

Start with a simple classification task. Pick one dimension that matters for your product. Write a prompt that judges outputs against that dimension.

Step 1: Define the Criteria

Be specific about what you measure. “Friendly tone” means nothing to a judge model. Define friendly: upbeat language, positive framing, absence of jargon, direct address to the user.

Bad criteria definition: “Is this response good?”

Good criteria definition: “A friendly response uses conversational language, addresses the user directly, avoids technical jargon, and maintains an upbeat tone throughout.”

Step 2: Specify the Labels

Give the judge model exactly two options for your first eval. Binary classification reduces ambiguity. The model picks one label. You get a clear signal.

Use descriptive text labels: “friendly” vs “robotic” or “helpful” vs “unhelpful.” Avoid numeric scales. LLMs struggle with numbers.

Step 3: Add Few-Shot Examples

Show the judge what good and bad look like. Include 2-3 examples of each label. The examples ground the abstract criteria in concrete text.

This step dramatically improves eval consistency. Without examples, the judge model interprets your criteria unpredictably. With examples, it has reference points.

Action item: Build a complete eval prompt with criteria definition, two labels, and four few-shot examples (two per label). Test it on five outputs from your current agent.

What Makes LLM-as-Judge Different from Unit Tests?

Software testing assumes deterministic behavior. Input A produces output B every time. LLM agents behave probabilistically. The same input produces different outputs across runs.

Unit tests check exact matches. LLM evals check semantic properties. Did the output address the user’s question? Did it maintain the required tone? Did it stay within factual bounds? These questions require judgment, not string comparison.

Integration tests rely on stable documentation. Agent evals rely on your data. The knowledge base, the user context, the conversation history shape every output. Your eval system must account for this variability.

The practical implication: you need more test cases and more sophisticated evaluation criteria. A unit test suite might have 100 cases. An agent eval data set might need 500-1000 examples to catch edge cases.

Action item: Identify three ways your current agent outputs vary based on context (user history, time of day, knowledge base state). Design eval criteria that account for this variation.

How Do You Scale from One Example to a Data Set?

Single-row evaluation tells you almost nothing. You need data sets of representative examples to catch systematic issues. Start with 10-15 examples. Scale to 100+ as your system matures.

Building Your First Data Set

Pull examples from production data. Real user queries expose patterns synthetic data misses. Select examples that cover your main use cases plus known edge cases.

Structure each example with:

• Input: The query or context the agent received
• Output: The response the agent generated
• Metadata: User segment, session type, any relevant context

Running Batch Evaluations

Run your eval prompt against every example in the data set. Record the label for each row. Calculate the percentage that pass your criteria.

A single eval run gives you a baseline. Compare against that baseline when you change prompts, models, or system configuration. Regressions become visible immediately.

Iterating on Low Scores

Low pass rates reveal improvement opportunities. Examine failed examples manually. Identify patterns. Update your agent prompts or your eval criteria based on findings.

Sometimes the agent needs improvement. Sometimes your eval criteria are too strict. The data tells you which.

Action item: Create a data set of 15 examples from your production logs. Run your eval against all 15. Document the pass rate as your baseline.

How Do You Validate Your Eval System?

LLM judges hallucinate too. You cannot trust an eval system without checking it against human judgment. The workflow: label examples yourself, compare to LLM labels, measure agreement.

Creating Human Labels

Go through your data set row by row. Apply your own judgment using the same criteria you gave the LLM judge. Record your labels separately from the LLM labels.

This step takes time. A 100-row data set might take 2-3 hours to label. The investment pays off in eval reliability.

Measuring Agreement

Compare human labels to LLM labels for each row. Calculate the match rate. High agreement (80%+) means your eval works. Low agreement means your eval prompt needs iteration.

Check disagreements individually. Sometimes the human was wrong. Sometimes the LLM was wrong. Sometimes the criteria are ambiguous and need clarification.

Iterating on the Eval Prompt

Treat your eval prompt like a product prompt. It needs refinement. Add examples that cover edge cases. Clarify criteria that caused disagreements. Rerun and remeasure.

The goal: an eval system that matches human judgment reliably. Once you have that, you can trust automated eval at scale.

Action item: Label 20 examples from your data set manually. Compare to LLM labels. Calculate match rate. If below 80%, identify the three most common disagreement patterns.

What Does Eval as Requirements Look Like?

Traditional PRDs describe desired behavior in prose. AI features need executable requirements. Eval data sets and eval prompts serve this function.

Instead of writing “the agent should respond helpfully,” you provide:

• A data set of 50 queries with expected helpful response characteristics
• An eval prompt that defines helpful and classifies outputs
• A target pass rate (e.g., 90% of outputs labeled helpful)

Your engineering team builds against these concrete criteria. Acceptance testing runs the eval. The feature ships when it hits the target pass rate.

This approach eliminates subjective debates about quality. The eval defines quality. The data proves quality. Disagreements become discussions about eval criteria, not personal opinions.

Action item: Convert one requirement from your current AI feature spec into eval format. Write the eval prompt. Create a 10-example test data set. Define the pass rate threshold.

Where Does the PM Own the Eval Workflow?

PMs own the end product experience. That ownership extends to defining and validating eval criteria. The PM decides what “good” means. The eval encodes that decision.

Prompt writing responsibility splits between PM and engineering. The PM specifies requirements and acceptance criteria. Engineering implements prompts that meet those criteria. Both iterate based on eval results.

Data set curation falls to the PM. You know the use cases. You know the edge cases. You know what matters to users. Engineering can help extract production data, but you select what goes into the eval data set.

Human labeling works best with domain experts. For consumer products, that might be the PM. For specialized domains, that might be subject matter experts. The PM coordinates the labeling effort and ensures consistency.

Action item: Map your current AI feature workflow. Identify who owns eval definition, data set curation, human labeling, and pass/fail decisions. Fill gaps where ownership is unclear.

What Tools Support This Workflow?

Arize provides an integrated platform for traces, evals, and experiments. You see agent behavior, run eval against production data, and compare prompt variations in one interface. The platform handles scale when you move beyond spreadsheets.

Open-source alternatives exist. Phoenix (Arize’s open-source offering) provides similar workflows without enterprise features. LangSmith from LangChain offers tracing and eval. PromptLayer focuses on prompt versioning and testing.

Spreadsheet workflows work for early stages. Export production examples. Run eval prompts manually or via API. Record results in columns. Compare to human labels. This approach breaks down past 100 examples.

The key capability: connecting production data to eval workflows. Whatever tool you use, it must let you pull real examples, run systematic evaluation, and track quality over time.

Action item: Set up tracing for one agent in your system. Export 10 production examples. Run your eval prompt against them. Record the workflow time.

What Results Should You Expect?

Eval systems deliver three outcomes: baseline metrics, regression detection, and iteration velocity.

Baseline metrics tell you where you stand. Your agent passes the friendliness eval 73% of the time. The accuracy eval shows 89% pass rate. These numbers become your reference points.

Regression detection catches problems early. After a prompt change, friendliness drops to 65%. You catch this in pre-production eval, not through user complaints. Rollback or iterate before shipping.

Iteration velocity increases dramatically. You test prompt variations systematically instead of reviewing individual outputs. A/B experiments run against data sets in minutes. Decisions rely on data, not intuition.

Teams report 2-3x faster iteration cycles once eval systems mature. The time investment in setup pays back within weeks.

Action item: Define three metrics you want to track for your primary agent. Build eval prompts for each. Run baseline measurements. Schedule weekly reruns.

Final Takeaways

Eval systems replace gut-check reviews with systematic quality measurement. The upfront investment saves weeks of production debugging.

LLM-as-judge prompts classify outputs against defined criteria. Use text labels, not numeric scores. Add few-shot examples for consistency.

Data sets of 50-100 examples catch issues that single-row reviews miss. Pull from production data. Cover main use cases and edge cases.

Human labels validate your eval before you trust it at scale. Match rates above 80% indicate reliable automation.

The PM who controls eval criteria controls product quality. Own the definition of “good.” Build acceptance testing around eval pass rates.