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Build Your Own Eval System

Everyone ships AI. Nobody measures it.

In 30 minutes you'll build an eval system — the skill that separates AI hobbyists from professionals.

It starts with ranking five responses.

Chapter 1: Vibes Are Not Metrics

You built an AI feature. Your boss asks "how good is it?" You say "it seems pretty good."

That's not an answer.

🔗 Connection: You got a taste of evaluation in Build Your Own Fine-Tuned Model (Chapter 5) — comparing your model's outputs against a baseline. This course goes deeper: systematic metrics, automated judges, and eval pipelines that run themselves. → Build Your Own Fine-Tuned Model

The Ranking Exercise

Below are 5 AI responses to the question: "How do I make pasta?"

Click to rank them from best (1) to worst (5):

Response A: Boil water, add pasta, cook for 8-12 minutes depending on type, drain, and serve with sauce.
Response B: To make pasta: 1) Fill large pot with water, add salt, bring to rolling boil 2) Add pasta, stir occasionally 3) Cook according to package directions (usually 8-12 min) 4) Drain, reserve pasta water 5) Toss with sauce, adding pasta water if needed for consistency.
Response C: Pasta is made from wheat flour and water. The ancient Romans ate a form of pasta. You should boil it in water until it's done.
Response D: Put the pasta in cold water and wait. It will eventually get soft. Then eat it.
Response E: For perfect pasta, use 4-6 quarts of water per pound of pasta. Salt generously (1 tablespoon per quart). Bring to vigorous boil before adding pasta. Stir within first minute to prevent sticking. Test for doneness 1-2 minutes before package time suggests. Reserve 1 cup pasta water before draining. Finish cooking in sauce for 1-2 minutes for better flavor integration.

📚 Go Deeper

Why Human Evaluation Fails (YouTube) Challenges in Evaluating LLMs (Paper)

Chapter 2: What to Measure

Different tasks need different metrics. The metric you choose shapes the product you build.

Accuracy, precision, recall, F1 for classification. BLEU/ROUGE for text generation. Human preference for open-ended tasks.

Build a Confusion Matrix

You're building a spam classifier. Drag the predictions into the right boxes:

Predicted: Not Spam
Predicted: Spam
Actually: Not Spam
0
True Negative
0
False Positive
Actually: Spam
0
False Negative
0
True Positive
Drag these predictions:
Email about Nigerian prince → Spam Work meeting invite → Not Spam Mom's birthday reminder → Spam Crypto scam → Not Spam Fake lottery winner → Spam Friend's vacation photos → Not Spam
0%
Precision
Of emails marked spam, how many actually were?
0%
Recall
Of all spam emails, how many did we catch?

📚 Go Deeper

Precision vs Recall Explained (YouTube) BLEU vs Human Evaluation (Paper)

Chapter 3: Test Sets

Your eval is only as good as your test data. A good test set is the most valuable thing in your AI project.

Build a test set: input → expected output pairs. Include edge cases, adversarial examples, and typical queries.

Build a Customer Support Test Set

Create 10 test cases for a customer support bot:

📚 Go Deeper

Building Better Test Sets (YouTube) Adversarial Examples in NLP (Paper)

Chapter 4: Automated Eval

Running evals by hand doesn't scale. Use an LLM to judge another LLM's outputs (LLM-as-judge).

LLM judges agree with humans 80% of the time — enough to catch regressions.

Build an LLM Judge

Write an evaluation prompt:

📚 Go Deeper

LLM-as-Judge Tutorial (YouTube) Judging LLM-as-a-Judge (Paper)

Chapter 5: The Eval Loop

Change something (prompt, model, temperature) → run eval → compare.

Every improvement to your AI should start and end with an eval.

A/B Test: Prompt Variations

Test two versions of your system prompt:

Version A: Basic

0.0
Average Score

Version B: Detailed

0.0
Average Score

Eval Dashboard

Track your metrics over time:

Performance Over Time

Week 1: 3.2/5
Week 2: 3.5/5
Week 3: 4.2/5
Week 4: 4.5/5

📚 Go Deeper

LMSYS Chatbot Arena OpenAI Evals Framework Building Eval Pipelines (YouTube)

🎉 You Built an Eval System!

You can now measure, compare, and improve any AI system.

This is the skill that separates professionals from hobbyists.

🧠 Final Recall

Test yourself. No peeking. These questions cover everything you just learned.

1. Why are "vibes" insufficient for evaluating AI systems?





2. In a spam classifier, what does "precision" measure?





3. What makes a good test set for AI evaluation?





4. What is "LLM-as-judge" and why is it useful?





5. What is the purpose of A/B testing in AI evaluation?





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