If you have ever launched a feature powered by the LLM, then you might have got users or leadership asking questions about its reliability or “How do we measure improvement?” Then you know that the core challenge in making AI product is not about building the AI product but is can we trust what we built. That’s where Evals come in.
In traditional software, QA tests and analytics tell us if features are working or not. But AI products—especially those built with large language models (LLMs)—produce probabilistic, non‑deterministic outputs. The same input can produce different results, and the output quality is not about just correctness but also about it’s safety, relevance, fairness and alignment with the user intent. Therefore, mastering Evals is not optional—it’s indispensable.
What Are AI Evals?
An Eval is the systematic measurement that tells about the quality of the AI pipeline.
It is not a single test or a number but is a structured process that generates the interpretable insights into how good an LLM or its AI components performs on the real world tasks.
LLMs is not just like any traditional software because:
The same prompt can produce different outputs on different runs.
Outputs may be fluent and persuasive yet factually incorrect.
“Good enough” is subjective unless we define it measurable.
"Why": Evals as the New PRD
Evals are the new PRDs.
Know the "Definition of Quality": You cannot delegate "quality" to engineers because they don't always have the product intuition or domain expertise.
Stopping the "Vibe Check": Relying on manual, ad-hoc testing (vibes) is the biggest mistake. Evals turn subjective "feelings" into objective, repeatable data.
Direct Influence: Evals are the mechanism through which your product intuition actually influences model behavior.
Why AI Evals Are Mission-Critical for PMs
Goal | What It Means in Practice | AI Product Example | PM Value |
|---|---|---|---|
Align with Business Goals | Connect model outputs to measurable product success metrics, not just technical accuracy. | A support chatbot eval tracks whether higher intent-classification accuracy reduces ticket escalations and average handling time. | Ensures AI improvements directly impact business KPIs and justify investment. |
Detect & Diagnose Failure Modes | Identify where and why the model fails, not just overall performance. | Eval reveals the model consistently misclassifies refund-related queries as technical issues. | Helps prioritize fixes by impact instead of blindly retraining models. |
Reduce Product Risk | Evaluate safety, bias, and compliance alongside correctness. | Safety eval flags responses that provide medical advice without disclaimers or show demographic bias. | Protects users, brand trust, and regulatory compliance. |
Enable Continuous Improvement | Use Evals as an ongoing feedback loop, not a one-time launch gate. | Comparing eval scores across model versions after prompt or data changes. | Enables confident iteration and long-term AI quality improvement. |
Why LLM Pipelines Fail — and How AI Evals Save You
🧠 The R-F-R-G-E-S-R-L evals framework-
Rubric → Failure-Mode → Robustness → Grounding → End-to-End → Safety → Regression → LLM-as-Judge
First define quality (Rubric) → whether outputs meet subjective quality standards (helpfulness, relevance, tone, completeness),
then find who it fails for (Failure-Mode) → Hidden high-impact errors,
break it on purpose (Robustness) → Fragility to real inputs,
check if it’s lying (Grounding) → Hallucinations,
see if the system works (End-to-End) → Broken pipelines,
make sure it’s safe (Safety) → Harm, policy violations,
ensure nothing regressed (Regression) → Silent quality drops,
and finally scale review (LLM-as-Judge) → Scalable judgment.
LLM Pipeline Failure | What Goes Wrong in the Product | Eval Technique That Catches It | What the Eval Measures | PM Outcome |
|---|---|---|---|---|
Unclear success definition | Outputs “look okay” but don’t meet user expectations | Rubric-based / Reference-free Evals | Helpfulness, relevance, completeness, tone | Turns subjective quality into measurable launch criteria |
Over-reliance on averages | Critical edge cases are hidden by high overall scores | Failure-mode & segmented Evals | Performance by query type, topic, or risk level | Prioritizes fixes by user impact |
Poor generalization to real inputs | Model breaks on typos, slang, ambiguous prompts | Robustness / Stress-testing Evals | Stability under noisy, adversarial, or malformed inputs | Finds breaking points before users do |
Hallucinations & overconfidence | Fluent but incorrect answers erode trust | Faithfulness / Grounding Evals | Whether outputs are supported by source data | Prevents confident misinformation |
Weak retrieval or tool usage | Right model, wrong context or tools | End-to-End / Workflow Evals | Retrieval quality, tool accuracy, final answer correctness | Improves full system reliability, not just the model |
Safety & bias blind spots | Harmful, biased, or policy-violating outputs | Safety & Bias Evals | Toxicity, fairness, policy compliance | Reduces legal, ethical, and brand risk |
One-time evaluation mindset | Quality degrades silently over time | Continuous / Regression Evals | Performance changes across versions | Enables confident iteration and prevents regressions |
Slow or subjective human review | Inconsistent feedback and poor scalability | LLM-as-a-Judge Evals | Scaled quality judgments using consistent rubrics | Speeds up iteration without losing quality signal |
Challenges in AI Evals
Evaluating AI, especially LLMs or generative systems, is harder than traditional ML because of multiple factors:
1️⃣ Subjective Quality
Generative outputs are rarely “right” or “wrong” in a binary sense.
Examples: Summaries, chat responses, creative writing.
Challenge: Different humans may judge the same output differently.
Implication: Requires rubric-based, human, or LLM-as-judge evals to quantify subjective quality.
2️⃣ Long-Tail / Rare Failure Modes
Most evaluation datasets focus on common scenarios.
Real users often trigger edge cases that break models.
Example: Chatbots misclassifying rare intents, hallucinations on niche queries.
Implication: You need failure-mode and stress-testing evals to catch high-impact but low-frequency issues.
3️⃣ Contextual / Multi-Step Dependencies
Modern AI pipelines aren’t just single-step predictions—they involve:
Retrieval → reasoning → tool usage → output
A single mistake can propagate silently.
Implication: Must run End-to-End workflow evals to measure system-level correctness.
4️⃣ Dynamic / Evolving Inputs
Real-world data changes constantly.
Training distribution ≠ user distribution → data drift.
Implication: Continuous and regression evals are needed to detect degradation over time.
5️⃣ Safety, Bias & Ethical Concerns
AI outputs may be technically “correct” but unsafe, biased, or unethical.
Example: Toxic completions, demographic bias, policy violations.
Implication: Safety & bias evals are mandatory, but they require careful prompt design and red-teaming.
6️⃣ Lack of Ground Truth
Many tasks have no perfect reference output.
Example: Creative text, advice, or summarization.
Challenge: Hard to compute BLEU, ROUGE, or exact match scores.
Implication: Need human judgment, reference-free metrics, or embedding-based similarity.
7️⃣ Scalability
Human evaluation is slow, inconsistent, and expensive.
Generative outputs are numerous (LLMs generate multiple completions per prompt).
Implication: Must combine LLM-as-judge evals and automated scoring to scale.
8️⃣ Metric Overload / Misalignment
Many metrics exist, but high technical performance does not always correlate with business impact.
Example: High BLEU in summarization doesn’t mean users find summaries helpful.
Implication: PMs need to choose metrics that map directly to business goals.
AI evals are hard because outputs are subjective, failure modes are long-tail, pipelines are multi-step, inputs are dynamic, safety is critical, ground truth is rare, human review is slow, and technical metrics don’t always reflect product impact.
By understanding these challenges and implementing structured evals, you can turn uncertainty into actionable insights, build more reliable AI products, and make confident product decisions.
In the next tutorial, we will dive deeper into the different types of evaluation metrics, exploring how to measure AI performance effectively and tie it directly to business impact.