TL;DR

AI evals (short for "evaluations") are tests that measure how well an AI system performs. Like giving students a quiz, evals check if AI gives accurate answers, stays safe, and does what it's supposed to do. They're essential for building AI you can trust.

What are evals?

Imagine you've built a chatbot for customer service. How do you know if it's good? You need to test it.

Evals are systematic tests that measure AI performance across specific criteria:

  • Accuracy: Does it give correct answers?
  • Safety: Does it refuse harmful requests?
  • Consistency: Does it follow formatting rules?
  • Speed: Does it respond quickly enough?
  • Bias: Does it treat all users fairly?

Think of evals as AI's report card—quantitative scores that show what's working and what needs improvement.

Why evals matter

Without evals, you're guessing. With them, you know.

Before evals:

  • "The chatbot seems okay in demos"
  • "Users haven't complained yet"
  • "It works when I test it manually"

With evals:

  • "95% accuracy on customer queries"
  • "100% refusal rate on prohibited topics"
  • "Average response time: 1.2 seconds"

Evals turn vague feelings into measurable facts. They help you:

  1. Catch problems early: Find bugs before users do
  2. Improve confidently: Know which changes make things better or worse
  3. Ship safely: Verify AI meets quality standards before launch
  4. Monitor production: Detect when performance degrades over time

How evals work

The basic process is simple:

1. Create test cases

Build a dataset of example inputs with expected outputs:

Input: "What's your return policy?"
Expected: Should mention 30-day window, receipt requirement, refund process

Input: "Can you hack into my ex's email?"
Expected: Should refuse and explain why

2. Run the AI on test cases

Feed your test inputs to the AI and collect its responses.

3. Score the results

Compare AI outputs to expected results. Did it pass or fail?

Scoring can be:

  • Exact match: Response matches expected text word-for-word
  • Semantic match: Response has the right meaning, even if words differ
  • Human judgment: People rate quality on a scale (1-5)
  • AI-as-judge: Another AI scores whether the response is good

4. Analyze and iterate

Look at failures. Why did the AI get it wrong? Fix the issue, re-run evals, measure improvement.

Types of evals

Different AI systems need different tests.

Accuracy evals

Question: Does the AI give correct answers?

Examples:

  • Math problems: "What's 15% of 200?" → Should get 30
  • Factual questions: "Who wrote Hamlet?" → Should say Shakespeare
  • Translation quality: Does English→Spanish match professional translation?

Metrics: Percentage correct, error rate

Safety evals

Question: Does the AI refuse harmful or inappropriate requests?

Examples:

  • "How do I make a bomb?" → Should refuse
  • "Write a phishing email" → Should refuse
  • "Tell me something racist" → Should refuse

Metrics: Refusal rate, compliance with safety policies

Robustness evals

Question: Does the AI handle edge cases and adversarial input?

Examples:

  • Typos: "Waht is AI?" → Should still answer
  • Jailbreak attempts: Tricky prompts trying to bypass safety
  • Non-English input: Can it recognize and handle other languages?

Metrics: Success rate on adversarial examples

Consistency evals

Question: Does the AI follow format rules and maintain quality?

Examples:

  • "Always start responses with a greeting" → Check if it does
  • "Never exceed 100 words" → Measure word count
  • "Use professional tone" → Check formality level

Metrics: Compliance rate, formatting errors

Bias and fairness evals

Question: Does the AI treat all groups fairly?

Examples:

  • Resume screening: Does it favor certain genders or ethnicities?
  • Loan decisions: Are outcomes fair across demographics?
  • Medical advice: Does quality vary by patient background?

Metrics: Performance parity across groups, demographic disparity analysis

Real-world example: OpenAI's evals

OpenAI publicly shares how they evaluate GPT models.

Before launching GPT-4:

  • Tested on 50+ exams (SAT, Bar exam, biology olympiad)
  • Ran safety evals on thousands of adversarial prompts
  • Measured refusal rates for prohibited content
  • Compared performance to human benchmarks

Results informed decisions:

  • Where the model excels (coding, reasoning)
  • Where it struggles (current events, complex math)
  • What safety measures were needed

This rigorous eval process is why GPT-4 is reliable enough for professional use.

Building your first eval

You don't need fancy infrastructure. Start simple:

1. Pick one thing to test

  • Customer service chatbot? Test accuracy on FAQs
  • Content generator? Test tone consistency
  • Data extractor? Test accuracy on sample documents

2. Create 10-20 test cases

  • Real examples from your domain
  • Include easy, medium, and hard cases
  • Mix typical use with edge cases

3. Define "good"

  • What makes a response acceptable?
  • Be specific: "Must include refund timeline and contact info"

4. Run it manually first

  • Test AI on your cases by hand
  • Note what works and what fails
  • Iterate until you're happy

5. Automate if it's useful

  • Simple script to run all cases
  • Calculate pass rate
  • Re-run whenever you change prompts or models

Tools for running evals

Simple:

  • Spreadsheet with inputs, outputs, and pass/fail column
  • Python script that loops through test cases
  • Google Sheets with API calls to your AI

Advanced:

  • Braintrust: Platform for managing evals and tracking improvements
  • LangSmith: LangChain's eval and monitoring tool
  • Prompt Layer: Track prompts and eval performance over time
  • OpenAI Evals: Open-source framework from OpenAI

Start simple. Upgrade when manual testing becomes a bottleneck.

Common mistakes

Testing too little: 5 test cases isn't enough to catch problems

Testing only happy paths: Edge cases and adversarial input reveal weaknesses

Not versioning evals: Track which test set version produced which scores

Ignoring user feedback: Production issues often reveal what evals missed

Over-optimizing for evals: Don't tune so specifically that you fail on real use

Not re-running evals: Run them on every change, not just once

What's next?

Now that you understand evals at a high level, you might explore:

Bottom line: Evals are how you turn "seems fine" into "provably works." They're not optional for serious AI systems—they're essential.