How should a product manager evaluate LLM quality without being a machine learning engineer?

Focus on building a golden dataset of 100-500 representative examples with expected outputs, then use a combination of automated metrics (BLEU, ROUGE for reference-based tasks; LLM-as-judge for open-ended tasks) and structured human evaluation. You don't need to understand the math behind every metric -- you need to define what good looks like for your use case, build test cases that represent it, and establish a consistent evaluation pipeline that runs with every model change.

What is the LLM-as-judge approach and when should you use it?

LLM-as-judge uses a separate, typically more capable language model to evaluate the outputs of your production model against defined quality criteria. It is most useful for open-ended generation tasks where reference-based metrics fail (creative writing, conversational responses, summaries of novel content). It is faster and cheaper than human evaluation while correlating well with human judgments when the judging prompt is carefully designed with clear rubrics and examples.

How large should a golden dataset be for LLM evaluation?

Start with 100-200 examples covering your core use cases and known edge cases. Expand to 500+ as your product matures. The key is coverage, not volume: your dataset should represent the full distribution of real user inputs, including edge cases, adversarial inputs, and examples from underrepresented user segments. A focused dataset of 200 well-curated examples is more valuable than 2,000 randomly selected ones.

LLM Evaluation Framework

Quick Answer (TL;DR)

Evaluating LLM quality requires five complementary approaches: Reference-based metrics (BLEU, ROUGE -- comparing outputs to known-good answers), Reference-free scoring (perplexity, coherence, fluency -- measuring output quality without ground truth), LLM-as-judge (using a stronger model to evaluate a weaker one), Human evaluation (expert or crowd assessment against rubrics), and Golden datasets (curated test sets representing your product's real-world inputs). No single approach is sufficient. Product managers should combine automated metrics for speed with human evaluation for depth, all anchored to a golden dataset that represents their specific use case.

What Is the LLM Evaluation Framework?

Language model evaluation is the process of systematically measuring how well an LLM performs for your specific product use case. It is a critical skill for product managers building with LLMs, because without reliable evaluation, you can't make informed decisions about model selection, prompt engineering, fine-tuning, or feature quality.

The challenge is that LLM outputs are open-ended text. Unlike traditional software where you can write assert output == expected, evaluating whether a generated paragraph is "good" requires nuanced judgment. Is the summary accurate? Is the response helpful? Is the generated code correct? Is the chatbot's tone appropriate? Each of these questions requires a different evaluation approach.

This framework emerged because early LLM product teams repeatedly made the same mistake: they eyeballed a few model outputs, decided it "looked good," and shipped to production. Then user complaints revealed systematic quality issues that anecdotal testing never caught: hallucinations in specific domains, inappropriate tone with certain user types, degraded performance on long inputs, or inconsistent formatting.

The framework provides PMs with a structured evaluation strategy that catches these issues before users do. It's designed to be practical -- you don't need a PhD in NLP to implement it, but you do need to invest deliberate effort in building evaluation infrastructure.

The Framework in Detail

Approach 1: Reference-Based Metrics

Reference-based metrics compare the model's output to a known-correct reference answer. They work well when there's a "right answer" you can define in advance.

Key Metrics:

BLEU (Bilingual Evaluation Understudy)

Originally designed for machine translation, BLEU measures the overlap of n-grams (word sequences) between the generated text and the reference text. Scores range from 0 to 1, with higher being better.

When to use: Translation, text-to-code generation, highly structured outputs

Limitation: Penalizes valid paraphrases. "The cat sat on the mat" and "A feline rested on the rug" score low despite meaning the same thing.

ROUGE (Recall-Oriented Understudy for Gisting Evaluation)

Measures the overlap between generated and reference text, with variants focusing on different granularities:

ROUGE-1: Unigram (single word) overlap

ROUGE-2: Bigram overlap

ROUGE-L: Longest common subsequence

When to use: Summarization, extractive question answering, information retrieval

Limitation: Rewards surface-level similarity without assessing semantic correctness

Exact Match (EM)

Binary: does the output exactly match the reference?

When to use: Classification, entity extraction, structured data generation (JSON, SQL)

Limitation: Too strict for free-form text; too lenient for structured outputs with multiple valid formats

F1 Score (token-level)

Measures the overlap of individual tokens between generated and reference text, balancing precision (are generated tokens correct?) and recall (are reference tokens present?).

When to use: Extractive QA, named entity recognition

Limitation: Treats all tokens as equally important

PM Guidance for Reference-Based Metrics:

Reference-based metrics are your first line of defense -- fast, automated, and reproducible. But they have a fundamental limitation: they can only evaluate outputs where you know the correct answer. For creative generation, conversation, and open-ended tasks, you need other approaches.

Set up a continuous evaluation pipeline that runs reference-based metrics on every model change (new prompt, new model version, new fine-tuning run). Track trends over time. A sudden drop in BLEU or ROUGE is a strong signal that something has changed.

Approach 2: Reference-Free Scoring

Reference-free metrics assess output quality without comparing to a ground truth. They're useful for tasks where there's no single correct answer.

Key Metrics:

Perplexity

Measures how "surprised" the model is by its own output. Lower perplexity indicates more fluent, natural-sounding text.

When to use: Detecting degraded outputs, comparing model versions

Limitation: Measures fluency, not accuracy. A model can fluently generate incorrect information with low perplexity.

Coherence Scoring

Automated assessment of whether the output is logically structured and internally consistent. Typically measured by embedding-based similarity between sentences or paragraphs.

When to use: Long-form generation, multi-paragraph outputs

Limitation: Heuristic-based; doesn't catch factual errors

Toxicity and Safety Scoring

Automated classifiers (Perspective API, OpenAI moderation endpoint, custom classifiers) that score outputs for harmful content.

When to use: Any user-facing text generation

Limitation: Classifiers have their own accuracy limitations; they can miss subtle harmful content or flag benign content

Factual Consistency (NLI-based)

Uses natural language inference models to check whether generated claims are supported by a source document. If the model is supposed to summarize or answer based on a document, NLI checks whether the output contradicts the source.

When to use: Summarization, document-grounded QA, RAG pipelines

Limitation: NLI models are themselves imperfect; high-quality but not foolproof

PM Guidance for Reference-Free Metrics:

Reference-free metrics are useful as automated guardrails. Set up toxicity scoring as a hard gate -- no output above a threshold reaches users. Use coherence and perplexity as regression detection signals. But never rely on reference-free metrics alone to assess quality; they measure surface properties, not whether the output actually helps the user.

Approach 3: LLM-as-Judge

LLM-as-judge uses a separate language model (typically a more capable one) to evaluate the outputs of your production model. The judging model scores outputs against criteria you define.

How It Works:

Define a rubric with clear criteria (accuracy, helpfulness, tone, completeness, conciseness)

Construct a judging prompt that presents the rubric, the input, and the model's output

Ask the judge model to score the output on each criterion (e.g., 1-5 scale)

Optionally provide the judge with a reference answer for comparison

Example Judging Prompt:

You are evaluating the quality of a customer support response.

User question: {user_question}
AI response: {model_output}

Rate the response on the following criteria (1-5 scale):

1. Accuracy: Is the information in the response factually correct?
2. Completeness: Does the response fully address the user's question?
3. Tone: Is the tone professional and empathetic?
4. Conciseness: Is the response appropriately concise without omitting important details?
5. Actionability: Does the response give the user clear next steps?

For each criterion, provide the score and a brief justification.

When LLM-as-Judge Works Well:

Open-ended generation where reference-based metrics fail

Tasks where quality is multidimensional (accuracy + tone + completeness)

High-volume evaluation where human review is too expensive

Rapid iteration cycles where you need fast feedback

When LLM-as-Judge Falls Short:

Highly specialized domains where the judge model lacks expertise

Tasks requiring real-world knowledge verification (the judge can be wrong too)

Adversarial evaluation (models tend to rate other models generously)

When the production model and judge model share the same biases

PM Guidance for LLM-as-Judge:

LLM-as-judge is the most practical evaluation approach for most LLM products. Invest time in crafting a clear rubric and judging prompt -- the quality of the rubric determines the quality of the evaluation. Calibrate the judge against human evaluations: run 100-200 examples through both human raters and the judge model, then measure agreement. If correlation is above 0.8, the judge is usable for automated evaluation. Recalibrate regularly.

Approach 4: Human Evaluation

Human evaluation is the gold standard for LLM quality assessment. It captures nuances that no automated metric can fully measure.

Types of Human Evaluation:

Expert Evaluation

Domain experts (lawyers for legal AI, doctors for medical AI, engineers for code AI) assess outputs for correctness, completeness, and appropriateness.

Cost: High ($50-200/hour for domain experts)

Quality: Highest -- catches domain-specific errors that generalist evaluators miss

Scale: Low -- expensive and slow

Best for: High-stakes applications, setting quality benchmarks, calibrating automated metrics

Crowdsourced Evaluation

General-purpose evaluators (via platforms like Scale AI, Surge, or Amazon Mechanical Turk) rate outputs against defined rubrics.

Cost: Moderate ($15-50/hour)

Quality: Good for general quality, poor for specialized domains

Scale: Medium to high

Best for: General quality assessment, preference ranking, A/B testing at scale

Internal Team Evaluation

Product team members evaluate outputs during development.

Cost: Low (opportunity cost of team time)

Quality: Variable -- risk of bias toward own product

Scale: Low

Best for: Early-stage development, quick sanity checks, building intuition

Evaluation Protocol Design:

To get reliable human evaluations, you need a rigorous protocol:

Define criteria clearly. "Rate helpfulness on a 1-5 scale" is ambiguous. "Rate helpfulness: 5 = fully answers the question with actionable next steps; 4 = mostly answers with minor gaps; 3 = partially answers; 2 = tangentially relevant; 1 = not relevant or harmful" is usable.

Use multiple evaluators. Each example should be rated by at least 2-3 independent evaluators. Calculate inter-rater agreement (Cohen's kappa or Krippendorff's alpha). If agreement is low, your rubric needs refinement.

Randomize and blind. Evaluators should not know which model produced which output. If comparing two models, randomize the order of presentation to avoid position bias.

Include calibration examples. Start each evaluation session with 5-10 examples where the "correct" rating is established. This anchors evaluators to a shared standard.

Approach 5: Golden Datasets

A golden dataset is a curated collection of input-output pairs that represents your product's real-world usage patterns. It's the foundation that makes all other evaluation approaches meaningful.

Building a Golden Dataset:

Step 1: Define coverage requirements.

Your golden dataset should cover:

The most common user inputs (the "head" of the distribution)

Important edge cases (very short inputs, very long inputs, ambiguous requests)

Known failure modes (inputs where the model has historically struggled)

Adversarial inputs (attempts to break or misuse the model)

Diverse user segments (different demographics, skill levels, use cases)

Step 2: Source examples from real usage.

The best golden dataset examples come from actual user interactions. During beta testing or initial deployment, log user inputs (with consent) and curate the most representative ones. Supplement with synthetic examples for edge cases you haven't observed yet.

Step 3: Create reference outputs.

For each input, create one or more reference outputs that represent "good" quality. For tasks with objective answers (QA, classification), this is straightforward. For open-ended tasks, create examples that represent the minimum acceptable quality level.

Step 4: Version and maintain the dataset.

Your golden dataset is a living artifact. As the product evolves, user patterns change, and new failure modes are discovered, update the dataset. Version it like code -- every change should be tracked and documented.

Golden Dataset Size Guidelines:

Product Stage	Recommended Size	Coverage Priority
Prototype	50-100 examples	Core use cases only
Beta	100-200 examples	Core use cases + major edge cases
Production	200-500 examples	Full coverage including adversarial inputs
Mature product	500-1,000+ examples	Comprehensive coverage with segment-specific subsets

When to Use This Framework

Scenario	Primary Evaluation Approaches
Selecting between LLM providers for a new feature	Golden dataset + reference-based metrics + LLM-as-judge
Evaluating prompt engineering changes	Golden dataset + LLM-as-judge + A/B testing
Assessing a fine-tuned model vs. base model	Golden dataset + all five approaches
Monitoring production quality over time	Automated metrics (reference-based + reference-free) + periodic human evaluation
Setting launch acceptance criteria	Human evaluation (expert) to establish the bar; LLM-as-judge for ongoing enforcement

When NOT to Use It

Your AI feature is deterministic. If the LLM is used for classification with constrained outputs (choosing from a fixed set of labels), standard classification metrics (accuracy, precision, recall) are sufficient. You don't need the full framework.

You're in a 24-hour hackathon. Use human eyeballing. Build proper evaluation later.

The LLM is a minor component. If the LLM generates a three-word label that's reviewed by a human before display, lightweight evaluation suffices.

Real-World Example

Scenario: A customer success platform is building an AI feature that drafts email responses to support tickets. The PM needs to evaluate whether the generated drafts are good enough for agents to use.

Golden Dataset: The team curates 300 support tickets from their historical data, spanning 8 product areas, 4 urgency levels, and tickets from both individual users and enterprise accounts. For each ticket, a senior support agent writes a reference response.

Reference-Based Metrics: ROUGE-L scores against reference responses give a baseline. Average ROUGE-L is 0.42 -- moderate overlap, which is expected since there are many valid ways to respond to a support ticket.

LLM-as-Judge: A GPT-4 class model evaluates each draft on five criteria: accuracy (does the response contain correct product information?), tone (professional and empathetic?), completeness (addresses all parts of the customer's question?), actionability (provides clear next steps?), and conciseness (appropriate length?). Average scores: accuracy 4.2/5, tone 4.5/5, completeness 3.8/5, actionability 4.0/5, conciseness 4.3/5. The completeness score reveals that the model often misses secondary questions in multi-part tickets.

Human Evaluation: 10 senior support agents each evaluate 30 drafted responses (blind, randomized). They rate each response as "send as-is," "edit lightly and send," or "rewrite from scratch." Results: 35% send as-is, 48% edit lightly, 17% rewrite. The PM sets a launch target of < 15% rewrite rate and > 40% send as-is rate.

Action: The team improves the prompt to explicitly address multi-part questions, retests, and achieves completeness score 4.3/5 and a human evaluation of 42% send as-is, 45% edit lightly, 13% rewrite -- meeting the launch criteria.

Common Pitfalls

Vibes-based evaluation. "I tried a few examples and it seemed good" is not evaluation. Systematic quality issues hide in the long tail of inputs. If you haven't tested at least 100 representative examples, you don't know your model's quality.

Metric fixation. Optimizing for BLEU or ROUGE without checking whether the metric correlates with user satisfaction. A model can achieve high ROUGE by copying input text into the output -- technically high overlap, practically useless.

Stale golden datasets. Your golden dataset was great six months ago, but your product has evolved, user patterns have shifted, and new edge cases have emerged. If your evaluation dataset doesn't represent current usage, your evaluation results are misleading.

LLM-as-judge without calibration. Deploying an LLM judge without verifying it agrees with human evaluators. The judge might have systematic biases (favoring longer responses, favoring certain styles) that your product doesn't want.

Ignoring failure modes. Evaluation averages hide catastrophic failures. A model with 95% average quality that produces harmful content for 1% of inputs has a serious problem. Analyze the worst outputs, not just the average.

Evaluating the wrong thing. Measuring whether the LLM output is linguistically good rather than whether it helps the user accomplish their goal. A perfectly written response that doesn't answer the question is a failure.

LLM Evaluation vs. Other Quality Approaches

Approach	Focus	Best Used For
This framework (5 approaches)	Comprehensive LLM output quality	Any LLM-powered product feature
Traditional QA testing	Software correctness (inputs -> expected outputs)	Deterministic product features
A/B testing	User behavior comparison between variants	Measuring the impact of LLM features on user metrics
Red teaming	Adversarial testing for safety and misuse	Safety-critical LLM applications
Benchmarking (MMLU, HumanEval, etc.)	General model capability assessment	Model selection before product integration

The LLM Evaluation Framework is complementary to all of these approaches. Use benchmarking for initial model selection, this framework for product-specific quality assessment, A/B testing for user impact measurement, and red teaming for safety validation. Together, they give you a complete picture of whether your LLM feature is ready for production.

LLM Evaluation Framework: A Product Manager's Guide to Measuring Language Model Quality