Evals for PMs: Ensuring AI Product Quality

June 7, 2026

Evals for PMs, or AI evaluations, are systematic tests designed to measure the quality of AI systems and are crucial for Product Managers to ensure AI product quality, especially given the non-deterministic nature of AI. Unlike traditional software testing, evals provide structured data to define what "good" means for AI features, moving beyond subjective "vibe checks" to data-backed decisions. By implementing robust evaluation criteria, PMs can gain confidence in AI features before they ship, integrating this feedback loop into the AI product development cycle.

What are AI Evals and Why PMs Need Them

AI evaluations, or AI evals, are systematic tests designed to measure the quality of AI systems. For Product Managers (PMs), understanding and implementing evals is critical for ensuring AI product quality, especially given the inherent challenges of non-deterministic systems. Unlike traditional software, AI outputs are probabilistic; the same input can yield different results, making manual testing insufficient for gauging reliability. This necessitates a robust AI feedback loop.

PMs need AI evals to move beyond subjective "vibe checks" to data-backed decisions. Evals provide crucial product judgment by defining explicit evaluation criteria for what "good" looks like for an AI feature. For instance, instead of merely observing a demo, a PM can use evals to test hundreds of scenarios, verifying that "accuracy improved 5% without regressing tone" across 200 test cases. This structured approach helps identify failure patterns through error analysis and guides prompt engineering efforts. Tools like the OpenAI platform allow PMs to configure prompts, models, and add "LLM as a judge" graders to score answers against defined criteria. This process enables PMs to make confident ship or hold decisions, ensuring that AI agents perform reliably and meet quality metrics before they impact users. Without evals, PMs risk shipping features that degrade silently, leading to frustrated customers and tarnished brand reputation.

Differentiating AI Evals from Traditional Software Testing

AI evals diverge significantly from traditional software testing methods due to the inherent characteristics of AI systems. Unlike deterministic software, where a given input consistently yields the same output, AI systems are non-deterministic and probabilistic. This means the same input can produce varied outputs, making binary pass/fail checks insufficient for assessing quality. Traditional testing often focuses on functional correctness and bug identification through scripted, predictable scenarios. In contrast, AI evals necessitate a focus on subjective quality criteria, nuance, and product judgment.

For instance, a traditional test might verify if a button click triggers a specific function. An AI eval, however, assesses if an AI agent's response is "helpful," "accurate," or "personalized"—qualities that require interpretation. This involves defining what "good" means for the AI's output, considering factors like context, tone, and user intent. While traditional testing might use unit tests or regression suites, AI evals often employ "LLM as a judge" graders to score responses against a rubric, or involve human annotation of AI outputs to identify failure patterns and perform error analysis. This shift from deterministic scripts to nuanced evaluation criteria is crucial for effective AI product development.

Defining "Good" for AI Features and Key Evaluation Criteria

For AI features, defining "good" moves beyond simple functional checks to encompass subjective quality criteria, demanding product judgment from PMs. This involves translating vague goals like "helpful," "accurate," or "personalized" into concrete, measurable quality metrics. For instance, instead of just aiming for "accurate" content generation, PMs might define "good" as "accuracy improved 5% without regressing tone" across 200 test cases, as seen in Braintrust's approach.

Establishing these criteria requires a structured approach to address the non-deterministic nature of AI. Key evaluation criteria often include:

Relevance: Does the AI output directly address the user's intent and context?
Accuracy/Factuality: Is the information provided correct and free from hallucinations?
Completeness: Does the AI provide sufficient information without being overly verbose?
Tone/Style: Is the output consistent with brand guidelines and appropriate for the user?
Safety/Bias: Does the AI avoid generating harmful, biased, or inappropriate content?

PMs define these criteria, which then guide the creation of test cases reflecting real user workflows, edge cases, and common failures. This allows for systematic error analysis to identify failure patterns. Tools like the OpenAI platform facilitate this by allowing PMs to configure prompts, models, and integrate "LLM as a judge" graders. These graders score outputs against the defined rubric, providing an AI scorecard and enabling "quality hill climbing" to iterate on prompts for better performance. This process ensures that "good enough" is consistently useful for the real job users need to accomplish, moving beyond "vibe checks" to data-driven confidence.

Components and Implementation of an AI Evaluation System

An effective AI evaluation system relies on three core components: a robust dataset, capable graders, and well-defined test cases. The dataset comprises real user workflows, edge cases, and common failure scenarios, moving beyond a few promising examples to hundreds of diverse inputs. This ensures comprehensive coverage and helps identify potential regressions. Graders, either human annotators or "LLM as a judge" systems, then score the AI's outputs against a predefined rubric. LLM-as-a-judge graders, for instance, can systematically assess responses for qualities like relevance, accuracy, and tone, providing an AI scorecard.

Implementing an eval system involves several practical steps. First, PMs must define "good enough" for the AI feature, translating subjective goals into concrete, measurable quality metrics. For example, instead of just "accurate," define it as "accuracy improved 5% without regressing tone" across 200 test cases. Next, create test cases from real user journeys and expected outputs. Then, configure prompts and models, often utilizing platforms like OpenAI, and integrate "LLM as a judge" graders. These graders assess outputs, allowing for error analysis to pinpoint failure patterns. This iterative process, termed "quality hill climbing," enables PMs to refine prompts and models for better performance, ensuring the AI consistently delivers useful results and providing data-driven confidence for shipping decisions.

Integrating Evals into the AI Product Development Cycle

Evals are not a one-time check but an ongoing process integrated throughout the AI product development cycle, from initial experimentation to post-launch monitoring. For Product Managers, this means defining "good enough" before development begins, running a comprehensive suite of evals before shipping, and continuously monitoring performance after launch. This ensures the quality bar is consistently met and adjusted as the model evolves.

During experimentation and testing, PMs utilize evals to refine prompts and models. For instance, using platforms like OpenAI, PMs can configure prompts, models, and integrate "LLM as a judge" graders to assess outputs against defined criteria. This iterative process, often called "quality hill climbing," allows for systematic error analysis to identify failure patterns and improve performance. Before shipping, a robust eval suite provides the data-driven confidence needed to make launch decisions, moving beyond subjective "vibe checks."

In production, evals transition into continuous monitoring. While traditional product analytics track metrics like adoption, retention, and drop-off, they don't assess the quality of AI output—whether the model hallucinated or created user risk. Evals bridge this gap by connecting to key product metrics such as retention, conversion, and feature engagement. Tools like Langfuse provide observability, allowing PMs to track eval scores and ensure the AI output remains useful, trusted, and safe. This creates a vital AI feedback loop, informing what to improve next and ensuring the AI agent consistently delivers on its promise.

Frequently Asked Questions

What is an AI eval?

An AI eval is a systematic process of evaluating an AI feature's performance against predefined quality metrics and user expectations, moving beyond subjective "vibe checks" to data-driven assessments. It involves using robust datasets, capable graders, and well-defined test cases to ensure the AI consistently delivers useful and reliable results.

Why do Product Managers need AI evals?

Product Managers need AI evals to objectively define and measure the quality of AI features, ensure they meet user needs, and make data-driven decisions about shipping and iterating on AI products. Evals help PMs move beyond subjective assessments to confidently deliver AI solutions that are useful, trusted, and safe.

How do AI evals differ from traditional software testing?

AI evals differ from traditional software testing by focusing specifically on the quality of AI output, such as accuracy, relevance, and absence of hallucinations, rather than just functional correctness. They involve assessing how well an AI model performs its intended task and whether it creates user risk, often using human annotators or "LLM as a judge" systems.

What are the key components of an AI evaluation?

The key components of an AI evaluation system are a robust dataset comprising diverse user workflows and edge cases, capable graders (either human or "LLM as a judge" systems) to score outputs, and well-defined test cases that translate subjective goals into measurable quality metrics.

How can PMs define "good" for an AI feature?

PMs define "good" for an AI feature by translating subjective goals into concrete, measurable quality metrics, such as "accuracy improved 5% without regressing tone" across a specific number of test cases. This involves establishing clear rubrics and success criteria that align with user needs and business objectives.

How do you integrate AI evals into the product development lifecycle?

AI evals are integrated throughout the product development lifecycle by defining "good enough" before development, running comprehensive evals before shipping, and continuously monitoring performance post-launch. This iterative process, often called "quality hill climbing," refines prompts and models, ensures consistent quality, and informs future improvements.

Conclusion

AI evals are no longer a nice-to-have but a critical component for any Product Manager building AI features. By embracing a systematic, data-driven approach to evaluation, PMs can confidently define, measure, and deliver AI solutions that truly meet user needs and drive business value. This structured methodology ensures that "good" for an AI feature is an objective, measurable standard, not just a subjective feeling.

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