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LLM Evals — Evaluation Systems for AI Products

LLM Evals

TL;DR: Evals are systematic ways to measure whether your AI is working. Unsuccessful AI products almost always share one root cause: failure to build robust evaluation systems. Without evals, teams plateau and resort to whack-a-mole problem-solving.

Why Evals Matter

Most AI projects fail not because the technology doesn’t work, but because teams can’t:

  • Assess quality objectively
  • Debug issues systematically
  • Measure improvement over time

The pattern: Team ships AI feature → gets complaints → makes random fixes → breaks something else → repeats forever.

With evals: Team ships AI feature → measures baseline → identifies specific failures → fixes targeted issues → confirms improvement → iterates.

The Three-Level Evaluation Hierarchy

Level 1: Unit Tests

What: Assertion-based tests that check specific behaviors.

When to use: During development, in CI/CD pipelines.

Examples:

  • Verify no UUIDs appear in customer-facing responses
  • Check that product prices are formatted correctly
  • Ensure responses don’t exceed character limits

How to build:

  1. Scope tests to specific features and user scenarios
  2. Generate test cases using LLMs (yes, use AI to test AI)
  3. Run tests in CI/CD with tracked metrics over time
  4. Update tests when you discover new failure modes

Key principle: Start simple. Even basic regex checks catch real problems.

Level 2: Human & Model Evaluation

What: Systematic review of AI outputs by humans and other AI models.

Three components:

1. Trace Logging Record every conversation, request, and response. Tools like LangSmith or custom logging work. The key: make it easy to see what happened.

2. Manual Review Humans look at actual outputs and judge quality.

Critical insight: “You must remove all friction from the process of looking at data.”

  • Build custom viewing tools with relevant context (customer data, expected outcomes)
  • Start with binary labels (good/bad) — not 1-5 scales
  • Review regularly, not just when things break

3. Automated Evaluation (LLM-as-Judge) Use powerful LLMs to critique outputs:

  • GPT-4 evaluates your GPT-3.5 outputs
  • Claude reviews your automated responses
  • Align the evaluator with human raters through iteration

Level 3: A/B Testing

What: Real user experimentation comparing versions.

When to use: Only for mature products ready for production validation.

Not a starting point: You need Levels 1-2 working first, or you won’t understand why Version A beat Version B.

Common Mistakes

MistakeWhy It Fails
Only doing prompt engineeringNo way to know if changes help or hurt
Delaying data examinationYou can’t fix what you don’t see
Sampling too aggressively earlyMiss edge cases that matter
Using generic frameworksYour domain needs custom evaluation
Complex rating scalesBinary (good/bad) is clearer and faster
Ignoring class imbalance99% “good” doesn’t mean you’re winning

The Eval Flywheel

Once you build evaluation infrastructure, it enables:

Fine-tuning: Curate high-quality training data from labeled traces Debugging: Search/filter traces to identify root causes Improvement: Measure whether changes actually work

These activities become nearly “free” once eval systems mature.

Practical Implementation

Start Here (Week 1)

  1. Add basic logging to capture inputs/outputs
  2. Create 10-20 test cases for your most important feature
  3. Review 10 real outputs manually per day

Build Up (Month 1)

  1. Automate test execution in CI/CD
  2. Build a simple dashboard to view traces
  3. Start using LLM-as-Judge for automated scoring
  4. Track metrics over time

Mature System (Quarter 1)

  1. Custom evaluation UI with full context
  2. Automated anomaly detection
  3. A/B testing infrastructure
  4. Feedback loop to fine-tuning pipeline

Tools & Frameworks

CategoryOptions
Trace LoggingLangSmith, Arize, HumanLoop, custom
VisualizationStreamlit, Metabase, custom dashboards
OrchestrationGitHub Actions, GitLab CI
SearchLilac (semantic search for traces)
TrackingExcel works fine for alignment tracking

Key Metrics

  • Pass rate: % of outputs meeting quality threshold
  • Precision: Of outputs flagged as good, how many actually are?
  • Recall: Of all good outputs, how many did we identify?
  • Latency: Time to generate response
  • Cost: Tokens/dollars per interaction

Warning: Track precision and recall separately, especially with imbalanced data.

Key Takeaways

  • Without evals, AI products plateau and never improve
  • Start with simple unit tests and manual review
  • Binary (good/bad) labels beat complex rating scales
  • Use LLMs to evaluate LLM outputs (LLM-as-Judge)
  • Build evaluation infrastructure before A/B testing
  • The eval system enables fine-tuning and debugging for “free”

Sources