Quick Answer (TL;DR)
LLM evals are systematic tests that measure how well your AI feature performs against defined quality criteria. As a PM, you do not need to write the evaluation code yourself, but you must own the eval strategy: deciding what to measure, building the test dataset, setting pass/fail thresholds, and interpreting the results to make ship/no-ship decisions. Think of evals as your AI product's test suite. Without them, you are shipping blind.
Summary: LLM evals are structured tests that let you measure AI output quality against defined criteria so you can make confident ship decisions.
Key Steps:
Time Required: 2-4 days to set up your first eval suite; 1-2 hours per eval run thereafter
Best For: PMs building or maintaining any product feature powered by an LLM
Table of Contents
What Are LLM Evals and Why PMs Should Care
An LLM eval is a structured test that sends a set of inputs to your AI system and scores the outputs against predefined criteria. It is the AI equivalent of a unit test suite, but instead of checking whether code returns the right integer, you are checking whether the model produces responses that are accurate, helpful, safe, and on-brand.
Without evals, you have no reliable way to answer critical product questions:
Evals replace gut feelings with data. They are the foundation of responsible AI product management and the mechanism that allows you to iterate on AI features with confidence.
Why This Is a PM Responsibility
Engineering owns the eval infrastructure. Data science may help design the scoring functions. But the PM must own the eval strategy because only the PM understands what "good" means from the user's perspective. A technically excellent response that misses the user's actual need is a product failure, and only the PM can define that boundary.
The PM's Role in Evaluations
Your job in the eval process is not to write Python scripts. It is to make four critical decisions:
1. Define Quality Dimensions
What attributes make a response "good" for your specific feature? Common dimensions include:
You will not evaluate all of these for every feature. A customer support chatbot might prioritize accuracy and tone. A code generation feature might prioritize correctness and completeness. Pick the 3-4 dimensions that matter most for your feature.
2. Create the Gold Standard
You decide what a great response looks like. This means writing or curating reference answers that represent the quality bar you want the model to hit. These reference answers become the benchmark for automated scoring.
3. Set Thresholds
You decide the pass/fail criteria. "We need 90% of responses to score 4 or higher on accuracy" is a product decision, not a technical one. Setting these thresholds requires balancing quality, cost, latency, and user expectations.
4. Make Ship Decisions
Eval results tell you whether a change improved or degraded quality. You decide whether the trade-offs are acceptable and whether the feature is ready to ship.
Choosing What to Evaluate
Not every aspect of your AI feature needs formal evaluation. Focus your eval efforts on the areas with the highest risk and the highest user impact.
High-Priority Eval Targets
Core task performance: Can the model do the main thing users expect? If your feature summarizes documents, evaluate summarization quality. If it answers questions, evaluate answer accuracy.
Edge cases and failure modes: What happens with unusual inputs? Empty inputs, extremely long inputs, adversarial inputs, inputs in unexpected languages, ambiguous queries. These are where models fail most visibly.
Safety and compliance: Does the model ever produce harmful, biased, or legally problematic outputs? This is non-negotiable for any user-facing feature.
Consistency: Does the model give substantially different answers to the same question on repeated runs? High variance is a product quality issue even if the average quality is acceptable.
Lower-Priority (But Still Important)
Latency and cost: Track these as operational metrics alongside quality evals. A response that takes 30 seconds is a product failure regardless of how accurate it is.
Format compliance: Does the model follow the output format you specified? JSON when you asked for JSON, bullet points when you asked for bullet points.
Building Your Evaluation Dataset
Your eval dataset is the single most important artifact in your eval process. A bad dataset produces misleading results that lead to bad ship decisions.
Dataset Composition
A strong eval dataset has three tiers:
Tier 1: Core scenarios (60% of dataset) represent the most common user inputs. Pull these from production logs if you have them, or generate representative examples based on user research. These cases should be straightforward and represent the bread-and-butter use case.
Tier 2: Edge cases (25% of dataset) represent tricky or unusual inputs: very short queries, very long queries, ambiguous requests, queries with typos, queries that require the model to say "I don't know." Edge cases are where quality differences between model versions become most visible.
Tier 3: Adversarial cases (15% of dataset) represent inputs designed to break the model: prompt injection attempts, requests for harmful content, attempts to get the model to contradict its instructions, inputs that try to extract system prompts. These test your safety boundaries.
Dataset Size
For most product features, you want:
Start with 50 and grow over time. Every production bug or user complaint should generate a new eval case.
Creating Reference Answers
For each test case, you need a reference answer or scoring rubric. There are two approaches:
Reference-based evaluation: You write the ideal answer and score model outputs by comparing them to the reference. Best for factual tasks where there is a clearly correct answer.
Rubric-based evaluation: You write scoring criteria and either human raters or an LLM judge scores outputs against the rubric. Best for creative or subjective tasks where multiple good answers exist.
Selecting Eval Metrics
Automated Metrics
These can be computed programmatically without human involvement:
Exact match: Does the output exactly match the expected answer? Useful for classification tasks and structured outputs.
String similarity (BLEU, ROUGE): How much word overlap exists between the model output and the reference answer? Useful as a rough quality signal for summarization and generation tasks, but can be misleading because two very different sentences can convey the same meaning.
LLM-as-judge: Use a separate, stronger LLM to grade outputs against a rubric. This is increasingly the standard approach for evaluating open-ended generation. The judge model scores each output on your defined dimensions and provides reasoning.
Regex and format checks: Does the output conform to the required format? Valid JSON, correct number of bullet points, within length limits.
Factual accuracy (retrieval-based): For RAG systems, check whether the model's claims are supported by the retrieved source documents.
Human Metrics
Automated metrics cannot fully capture quality for subjective or nuanced outputs. Include human evaluation for:
The Metric Stack
For most AI features, use this combination:
Setting Up Your First Eval
Step 1: Write Your Eval Spec
Before anyone writes code, document your eval strategy in a one-page spec covering the feature description, primary quality dimensions, dataset size target, scoring method, pass threshold, eval frequency, and ownership split between PM and engineering.
Step 2: Curate Your Initial Dataset
Spend one focused day building your first 50 test cases. Pull from real user queries from production logs or customer support tickets, edge cases identified during product design, adversarial cases from your security or trust team, and scenarios from user research and customer interviews.
Step 3: Define Your Scoring Rubric
Write a clear rubric for each quality dimension. Be specific enough that two different raters would give similar scores. A bad rubric says "Rate accuracy from 1-5." A good rubric says "Accuracy: 5 = all claims factually correct and verifiable. 4 = all major claims correct, minor details may be imprecise. 3 = mostly correct but contains one significant error. 2 = contains multiple errors that could mislead users. 1 = fundamentally incorrect or fabricated information."
Step 4: Run a Calibration Round
Before automating, do a manual eval run. Have 2-3 team members score 20 outputs independently. Compare scores. Where scores diverge, discuss and refine the rubric. This calibration step prevents weeks of wasted effort from a poorly defined rubric.
Step 5: Automate
Work with your engineering team to set up the eval pipeline: a script that runs all test cases through your AI feature, an LLM-as-judge or other automated scorer that grades each output, a results dashboard that shows scores broken down by dimension and test case category, and alerting when scores drop below your threshold.
Running Evals in Practice
When to Run Evals
Always run evals when:
Run evals on a schedule when:
Reading Eval Results
An eval run produces a results matrix showing the overall pass rate against your target, pass rates broken down by quality dimension (accuracy, relevance, tone, completeness), pass rates by test category (core scenarios, edge cases, adversarial), and regression data compared to the previous version. The PM's job is to assess whether dimension-level trade-offs are acceptable for users.
Debugging Failures
When eval scores drop, follow this process:
Interpreting Results and Making Decisions
The Ship Decision Framework
Use this framework to translate eval results into product decisions:
Green (ship it): All dimensions meet or exceed thresholds. No regressions from the previous version. Adversarial test pass rate is above your safety floor.
Yellow (ship with monitoring): Core scenarios pass. Some edge case degradation. You have monitoring in place to catch production issues. The improvement in primary dimensions outweighs minor edge case regressions.
Red (do not ship): Any safety or adversarial test failure. Core scenario pass rate below threshold. Significant regression in any primary dimension without a corresponding major gain elsewhere.
Communicating Results to Stakeholders
Executives and cross-functional partners do not want to see raw eval scores. Translate your results into business language:
Automating Evals in Your Pipeline
The Eval-Driven Development Workflow
Integrate evals into your development workflow the same way you integrate unit tests:
Monitoring Production Quality
Evals on test data are necessary but not sufficient. You also need production quality monitoring:
Common Eval Mistakes
Mistake 1: Evaluating on too few examples
Instead: Start with at least 50 test cases and grow to 200 over the first month.
Why: With fewer than 50 cases, a single flaky test result can swing your pass rate by 2% or more, making it impossible to distinguish real quality changes from noise.
Mistake 2: Not including adversarial cases
Instead: Dedicate at least 15% of your eval dataset to adversarial and safety-related inputs.
Why: Models can score 95% on happy-path cases while being trivially jailbroken. If you only test the happy path, you will miss your biggest risks.
Mistake 3: Using the eval dataset to tune prompts
Instead: Maintain a separate development set for prompt iteration. Only run the eval dataset for final scoring.
Why: If you optimize your prompts against your eval dataset, your scores will look great but will not generalize to real users. This is the AI equivalent of teaching to the test.
Mistake 4: Treating eval as a one-time setup
Instead: Add new test cases monthly from production failures, user complaints, and edge cases discovered in the wild.
Why: Your eval dataset should evolve with your product. The inputs users send in month six will be different from month one. A stale eval dataset gives you false confidence.
Mistake 5: Not calibrating your LLM judge
Instead: Validate your LLM judge against human ratings on at least 50 examples before trusting it.
Why: LLM judges have their own biases. They tend to be generous raters and can miss subtle quality issues that humans catch. Calibration ensures your automated scores are meaningful.
Getting Started Checklist
Day 1: Strategy
Day 2: Dataset
Day 3: Calibration
Day 4: Automation
Ongoing
Key Takeaways
Next Steps:
Related Guides
About This Guide
Last Updated: February 9, 2026
Reading Time: 15 minutes
Expertise Level: Intermediate
Citation: Adair, Tim. "How to Run LLM Evals: A Step-by-Step Guide for Product Managers." IdeaPlan, 2026. https://ideaplan.io/guides/how-to-run-llm-evals