Grokking LLM Evals
Learn how to test whether an LLM feature does its job, from reading its failures to statistics and production monitoring.
Course Overview
An LLM feature can pass every demo and still fail the customers who depend on it. Grokking LLM Evals teaches software engineers how to measure whether such a feature does its job, and how to decide from those measurements whether a change should ship. The course begins by reading real outputs and naming the ways they fail, then builds test sets from those failures. It covers the three kinds of graders, including how to check an LLM judge against human labels and correct a pass rate for the judge's own errors. It then applies these methods to retrieval-augmented generation, to agents, and to safety and robustness. The course gives statistics a full chapter: confidence intervals for pass rates, how many test cases a decision needs, paired comparisons of two versions, and noise from repeated runs. It then covers evals in production, public benchmarks, and two complete eval designs. Every method is independent of any eval tool, and most lessons include Python code that uses only the standard library and runs in the browser.
What you'll learn in Grokking LLM Evals
- Read real outputs of an LLM feature and turn its failures into a short, ranked list of failure modes.
- Grade outputs with code checks, reference answers and key fact checklists before reaching for a judge.
- Correct a measured pass rate for the judge's own errors, and know when the correction cannot be trusted.
- Evaluate agents by the state they leave behind, the rules they follow, and how reliably they succeed.
- Put a confidence interval on every pass rate and know how many test cases a decision needs.
- Run evals as release gates, grade sampled production traffic, and catch drift after a model update.
- Follow two complete eval designs: a help center RAG bot and a research agent that cites its sources.
- Build test sets from sampled traffic, generated cases and hard cases, with labels two people agree on.
- Write an LLM judge for one failure mode, test it for position and length bias, and validate it on human labels.
- Measure RAG retrieval and answers separately: recall at k, nDCG, faithfulness and unanswerable questions.
- Measure prompt injection, harmful requests, refusals of safe requests and sensitivity to small input changes.
- Compare two versions on the same cases with a paired test, and separate real changes from run-to-run noise.
- Read public benchmarks for what they are, and choose a model with your own product evals.
Course Content
Statistics for Evals
Public Benchmarks Versus Product Evals
Final Exam
What people say about our courses
About the Author
Arslan Ahmad
Industry Expertise & Leadership
Arslan Ahmad is the lead author of Grokking LLM Evals. As the founder of Design Gurus and a former FAANG hiring manager, he has worked at industry giants like Facebook (now Meta) and Microsoft.
He has conducted hundreds of system design interviews, giving him unique insight into what top tech companies look for in candidates.
The course also incorporates expertise from senior engineers at Google, Meta, Amazon, Microsoft, and Uber, ensuring you learn system design best practices from professionals who have built and scaled real-world systems.
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FAQs
What is Grokking LLM Evals?
It is a written course that teaches software engineers how to measure whether a feature built on a large language model does its job, and how to decide from those measurements whether a change should ship. It covers reading outputs, building test sets, grading, statistics, production practice, and two complete eval designs.
Who is this course for?
It is for software engineers who build, maintain or review LLM features, and for product managers and data scientists who work with them. You need basic Python. Every other term, including every statistical idea, is explained where it first appears.
Do I need a statistics background?
No. The course teaches the statistics it uses, starting from what a pass rate means, and then covers confidence intervals, how many test cases a decision needs, paired comparisons of two versions, noise from repeated runs, and testing many variants at once.
Is it tied to a particular eval tool or model provider?
No. Every method works with any provider and any eval tool. Tools are mentioned only as examples, and code that calls a model goes through one small function you can connect to any provider.
Can I run the code without an API key?
Yes, for most of it. Most lessons include Python that uses only the standard library and runs in the course player with the Run button, because each program carries its own data. Code that needs a live model, such as running an LLM judge, is shown for you to run on your own machine with your own key.
How does the course treat LLM judges?
It shows how to write a judge for one failure mode, test it for position and length bias, validate it against human labels with true positive and true negative rates, correct a measured pass rate for the judge's errors, and use pairwise comparisons when two versions must be ranked.
Does it cover RAG and agents?
Yes. RAG is evaluated as two measurements, retrieval and generation, with relevance labels, retrieval metrics, faithfulness, and unanswerable questions. Agents are evaluated by the state they leave behind, the rules they follow along the way, their reliability across repeated runs, and their cost.
How is this different from Grokking the AI System Design Interview?
The AI System Design course teaches how to design AI systems for an interview, with evaluation as one part of each design. This course teaches evaluation itself in depth for day-to-day engineering work, including judge validation, test set construction, and the statistics behind a release decision.
Does it cover evals in production?
Yes. It covers evals as release gates in continuous integration, logging traces for evaluation, grading a sample of live traffic, guardrails that run on every request, A/B tests for LLM features, user feedback signals, and drift after a model or input change.
What are the two complete designs?
One is a help center bot that answers questions from a payroll product's help articles, and the other is a research agent that answers library upgrade questions with citations. Each design goes from the first error analysis to a complete eval plan with release thresholds and production monitoring.
Is there any practice, or is it only reading?
Every lesson ends with practice questions and folded answers, and most lessons include code you can run and change. Each chapter ends with an adaptive assessment that draws a fresh set of questions on every attempt, and the course closes with an adaptive final exam across all chapters.
