AI Evaluation (Evals)

Build systematic evaluation frameworks for AI/LLM products to measure quality, catch regressions, and improve model performance.

When to Use

• Building product with LLM/AI components

• Need to measure AI output quality systematically

• Comparing models or prompts (A/B testing)

• Detecting regressions before deployment

• Benchmarking against competitors

• Improving AI accuracy over time

• Explaining AI decisions to stakeholders

Core Concept

AI Evaluation (Evals) ≠ Traditional Testing

Traditional software: Deterministic (same input → same output) AI/LLM systems: Probabilistic (same input → variable outputs)

Why Evals Are Hard:

• Outputs are subjective (is this "good" writing?)

• No single right answer (multiple valid responses)

• Edge cases are infinite (can't test everything)

• Models change behavior with updates

Solution: Build eval suites that:

• Define quality metrics (what is "good"?)

• Create representative test cases

• Measure systematically (automated + human)

• Track over time (catch regressions)

Workflow

Step 1: Define What You're Evaluating

AI Component Taxonomy

CLASSIFICATION TASKS:

• Sentiment analysis (positive/negative/neutral)
• Content moderation (safe/unsafe)
• Intent detection (user wants X)
• Entity recognition (extract names, dates)

Eval approach: Accuracy, precision, recall, F1 score

GENERATION TASKS:

• Text generation (summaries, responses, creative writing)
• Code generation (functions, scripts)
• Recommendations (suggest items, next actions)
• Translations (language → language)

Eval approach: Quality scores, human preference, task success rate

RETRIEVAL TASKS:

• Search (find relevant documents)
• Recommendation (rank items by relevance)
• Question answering (retrieve + synthesize answer)

Eval approach: Relevance, ranking quality (NDCG, MRR)

REASONING TASKS:

• Multi-step problem solving
• Complex decision making
• Causal inference

Eval approach: Correctness, reasoning quality, step-by-step validation

Step 2: Build Your Eval Dataset

Golden Dataset: Curated examples with known correct outputs.

Dataset Creation Framework

SIZE REQUIREMENTS:

• Minimum: 50-100 examples (manual review feasible)
• Good: 500-1000 examples (covers edge cases)
• Production: 5,000-10,000+ examples (statistical significance)

COMPOSITION:

1. Happy Path (40%) - Typical, well-formed inputs
2. Edge Cases (30%) - Unusual but valid inputs
3. Adversarial (20%) - Deliberately tricky inputs
4. Failure Cases (10%) - Invalid inputs (test error handling)

EXAMPLE (Book Recommendation AI):

Happy Path:

• "Recommend books like Harry Potter for my 10-year-old"
• "My kid loved Percy Jackson, what's next?"

Edge Cases:

• "Books for advanced reader (7yo but reads at 5th grade level)"
• "Fantasy but NO violence or romance"

Adversarial:

• "Best books" (too vague)
• "Books about [topic that doesn't exist for kids]"

Failure Cases:

• Gibberish input
• Adult content request

SOURCES FOR TEST CASES:

1. User Logs - Real queries (anonymized)
2. Team Brainstorm - Manual generation
3. Synthetic - GPT-4 to generate test cases
4. Competitor Comparison - Test against their outputs
5. Bug Reports - Historical failures

Step 3: Define Evaluation Metrics

Quantitative Metrics:

Metric Types

ACCURACY METRICS (Classification)

• Accuracy: (Correct predictions) / (Total)
• Precision: (True Positives) / (True Positives + False Positives)
• Recall: (True Positives) / (True Positives + False Negatives)
• F1 Score: Harmonic mean of precision and recall

Use when: Clear right/wrong answer (classification, extraction)

QUALITY METRICS (Generation)

• Coherence: Does output make sense? (1-5 scale)
• Relevance: Does output answer the question? (1-5 scale)
• Helpfulness: Is output useful to user? (1-5 scale)
• Safety: Is output safe/appropriate? (pass/fail)
• Hallucination Rate: % of outputs with false information

Use when: Subjective quality assessment needed

TASK SUCCESS METRICS

• Completion Rate: % of tasks successfully completed
• User Satisfaction: Thumbs up/down, NPS
• Time to Success: How long to achieve goal
• Retry Rate: % of users who re-prompt after first response

Use when: Evaluating end-to-end task performance

RANKING METRICS (Retrieval/Recommendation)

• MRR (Mean Reciprocal Rank): Average of 1/rank of first relevant result
• NDCG (Normalized Discounted Cumulative Gain): Quality of ranking
• Precision@K: % of top K results that are relevant

Use when: Evaluating search or recommendation quality

Qualitative Metrics:

Human Evaluation

PAIRWISE COMPARISON: Show human raters two outputs (A vs B), ask "Which is better?"

• Advantage: Easier than absolute rating
• Disadvantage: Slower, requires more comparisons

LIKERT SCALE RATING: Rate outputs 1-5 on dimensions (coherence, helpfulness, safety)

• Advantage: Fast, can aggregate scores
• Disadvantage: Subjective, rater disagreement

TASK COMPLETION: Can human complete task using AI output?

• Advantage: Measures real utility
• Disadvantage: Slow, expensive

RED TEAM REVIEW: Experts try to find failures (adversarial testing)

• Advantage: Finds edge cases
• Disadvantage: Not systematic

Step 4: Automated Evaluation Strategies

Use LLM as Judge:

LLM-as-Evaluator Pattern

CONCEPT: Use GPT-4 (or strong model) to evaluate outputs from your AI.

PROMPT TEMPLATE: "You are an expert evaluator. Rate the following AI response on:

1. Relevance (1-5)
2. Accuracy (1-5)
3. Helpfulness (1-5)

User query: {query} AI response: {response} Ground truth (if available): {truth}

Provide ratings and brief explanation."

ADVANTAGES: ✅ Scalable (can eval thousands of examples) ✅ Consistent (same rubric every time) ✅ Fast (seconds per eval) ✅ Cheap (pennies per eval)

DISADVANTAGES: ❌ Not 100% reliable (LLM judge can be wrong) ❌ Requires validation (compare to human ratings) ❌ Can miss nuanced failures

VALIDATION:

• Run LLM judge on 100-200 examples
• Have humans also rate same examples
• Calculate inter-rater agreement (Cohen's kappa)
• If agreement >70%, LLM judge is trustworthy

Step 5: Build Eval Pipeline

Continuous Evaluation System:

Eval Pipeline Architecture

COMPONENTS:

1. Test Suite Storage

   • JSON/CSV of test cases
   • Version controlled (git)
   • Tagged by category (happy path, edge case, etc.)

2. Runner Script

   • Iterate through test cases
   • Call AI system with each input
   • Collect outputs
   • Log latency, cost, errors

3. Scorer

   • Compare output to expected (if available)
   • Run automated metrics (accuracy, ROUGE, BLEU, etc.)
   • Call LLM judge for quality rating
   • Aggregate scores

4. Regression Detection

   • Compare current run to baseline
   • Flag significant drops (e.g., accuracy down >5%)
   • Alert team if regression detected

5. Reporting Dashboard

   • Visualize metrics over time
   • Drill down into failures
   • Compare models/prompts side-by-side

FREQUENCY:

• Pre-deploy: Every code/prompt change
• Nightly: Full suite run on production
• Weekly: Human review of sample outputs

Step 6: Common Eval Patterns

Evaluation Strategies by Use Case

RECOMMENDATION SYSTEMS

Test: Does user engage with recommendation?

Metrics:

• Click-through rate (CTR)
• Conversion rate (purchase, complete)
• Time spent with recommended item
• Diversity (not all same type)

Golden Dataset:

• Historical user behavior (X user liked Y, did they like Z?)
• Synthetic: "If user likes [A, B, C], recommend [D]?"

CONTENT MODERATION

Test: Does it correctly flag unsafe content?

Metrics:

• Precision (flagged = actually unsafe)
• Recall (actually unsafe = flagged)
• False positive rate (safe content flagged)

Golden Dataset:

• Curated examples of safe/unsafe content
• Edge cases (satire, context-dependent)

SUMMARIZATION

Test: Does summary capture key points?

Metrics:

• ROUGE score (overlap with reference summary)
• Factual consistency (no hallucinations)
• Compression ratio (length of summary / original)

Golden Dataset:

• Documents with human-written summaries
• Check: All key facts present, no false info

CODE GENERATION

Test: Does generated code work?

Metrics:

• Syntax correctness (parses without errors)
• Functional correctness (passes unit tests)
• Code quality (readable, efficient)

Golden Dataset:

• Programming problems with test cases
• Example: "Write function that reverses string" + 10 test cases

CONVERSATIONAL AI

Test: Does it handle multi-turn conversation well?

Metrics:

• Coherence across turns
• Context retention (remembers earlier messages)
• Task completion rate (user achieves goal)
• Safety (doesn't generate harmful content)

Golden Dataset:

• Scripted conversations with expected paths
• User logs (real conversations, anonymized)

Step 7: A/B Testing for AI

Compare models, prompts, or configurations:

AI A/B Testing Framework

SETUP:

1. Define variants (Model A vs. Model B, or Prompt v1 vs. v2)
2. Random assignment (50/50 split)
3. Define success metric (accuracy, user satisfaction, task completion)
4. Minimum sample size (depends on expected effect size)

METRICS TO TRACK:

• Primary: Quality (accuracy, preference, satisfaction)
• Secondary: Latency, cost, error rate
• Guardrails: Safety violations, user complaints

STATISTICAL SIGNIFICANCE:

• Run until p < 0.05 (95% confidence)
• Typically need 1,000-10,000 samples depending on effect size
• Use tools: Optimizely, LaunchDarkly, or custom

COMMON TESTS:

• Model comparison: GPT-4 vs. Claude vs. Gemini
• Prompt engineering: Version A vs. B
• Temperature: 0.7 vs. 0.9 (creativity vs. consistency)
• Context window: Include X vs. Y tokens of context

EXAMPLE: Variant A: GPT-4 with prompt v1 Variant B: GPT-4 with prompt v2

Metric: User thumbs up rate

• A: 70% thumbs up (n=500)
• B: 75% thumbs up (n=500)
• Result: B wins, p=0.03 (significant) → Ship prompt v2

Common Eval Mistakes

Anti-Patterns

❌ No Golden Dataset Testing AI without reference examples → Fix: Curate 100+ examples with expected outputs

❌ Testing Only Happy Path Ignoring edge cases and adversarial inputs → Fix: 30% of dataset should be edge cases

❌ Manual Eval Only Reviewing outputs one-by-one (doesn't scale) → Fix: Automate with LLM judge + spot-check humans

❌ No Regression Detection Shipping changes without comparing to baseline → Fix: Track metrics over time, alert on drops

❌ Vanity Metrics Measuring things that don't correlate with user value → Fix: Eval what matters (task success, user satisfaction)

❌ Overfitting to Eval Set Optimizing prompts specifically for test cases → Fix: Hold out test set, regularly refresh with new examples

Eval Tooling

Open Source:

• LangSmith (LangChain) - Eval framework for LLM apps

• Prompt flow (Microsoft) - End-to-end eval pipeline

• Weights & Biases - Experiment tracking

• Ragas - RAG evaluation framework

Commercial:

• Anthropic Console - Claude model evals

• OpenAI Evals - GPT model testing

• HumanSignal - Human annotation platform

Related Skills

• /building-with-llms

• Best practices for AI product development

• /ai-product-strategy

• Strategic AI product decisions

• /testing-strategies

• Traditional software testing

• /performance-optimization

• Optimize AI latency/cost

Last Updated: 2026-01-22