LLM Evals: Everything You Need to Know
hamel.dev- Category
- Other
- Type
- ARTICLE
- Builder
- @HamelHusain
- Added
- Jul 21, 2026
About
This document curates the most common questions Shreya and I received while teaching 700+ engineers & PMs AI Evals. Warning: These are sharp opinions about what works in most cases. They are not universal truths. Use your judgment. For a guided path through the rest of our evals work, use the AI evals topic hub . 👉 Want to learn more about AI Evals? Check out our AI Evals course . It’s a live cohort with hands on exercises and office hours. Here is a 25% discount code for readers. 👈 Listen to
What it can do
Answer common questions about LLM evaluation systems
Questions about product-specific LLM evals → Curated expert answers and sharp opinions
Teach a structured method for building an LLM-as-a-Judge
Domain expert pass/fail judgments and critiques on a dataset → An iterative LLM judge that drives business results
Guide error analysis to identify improvement opportunities
AI product output data and traces → Prioritized, highest-ROI improvements
Explain a multi-level evaluation framework
An AI product to evaluate → Evaluation strategy across unit tests, human/model eval, and A/B testing
Instruct on generating and curating synthetic evaluation data
Product context and use cases → Synthetic datasets for testing and fine-tuning
Provide an audio narration of the FAQ content
Text FAQ document → AI-narrated audio version
Offer a guided learning path through evals topics
User's learning goals → Links to topic hub, posts, and a live cohort course
Why it made the leaderboard
A dense, opinionated field guide to LLM evals covering error analysis, LLM-as-judge design, and critique shadowing — the practical workflows for actually improving AI products, not just theory. Useful if you're building AI features and drowning in traces without a systematic way to measure or debug quality.
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