LLM Evaluation for Builders: Applied Course
by Evidently AI
Ten code tutorials that turn 'seems fine' into a number you can defend.
Overview
A free applied course from Evidently AI, taught by co-founders Emeli Dral (CTO) and Elena Samuylova (CEO), delivered as a three-week email series of 10 or more video tutorials, each paired with an end-to-end code example. There is one short introductory session; everything after it is hands-on. The curriculum starts with automated quality checks and works up the ladder from deterministic validations, structural and format assertions, to model-based scoring, then spends its central modules on designing and tuning custom LLM judges so their scores actually align with human preference rather than merely producing a number. Separate modules cover reference-based versus reference-free evaluation methods, retrieval and generation quality for RAG systems including where synthetic data helps, adversarial testing to uncover model vulnerabilities and assess response safety, and evaluation of basic AI agents. Worked applications span summarization, classification and content generation, and a final module deals with the organisational side: how evaluation work gets divided across a team. Code runs on the open-source Evidently library together with the free tier of Evidently Cloud, so no commercial software is required; you supply an LLM API key and roughly a couple of dollars of request budget. The tutorial notebooks live publicly in the evidentlyai/community-examples repository under its `/learn` directory, so the material stays runnable independently of the email sequence. Basic Python is the only prerequisite, and the course is supported by a Discord community and live Q&A sessions.
At a Glance
- Topic
- Frameworks
- Level
- Intermediate
- Format
- Course
- Cost
- Free
- Duration
- ~3 weeks as an email series; 10+ video tutorials, self-paced
- Provider
- Evidently AI
- Hands-on
- Yes — code/exercises
- Certificate
- None
What You’ll Learn
- ✓Layer deterministic validations and model-based scoring into one evaluation suite
- ✓Design a custom LLM judge and tune it to match human preferences
- ✓Choose between reference-based and reference-free evaluation for a given task
- ✓Measure retrieval quality and generation quality separately in a RAG system
- ✓Run adversarial tests to surface model vulnerabilities and assess response safety
- ✓Evaluate a basic AI agent rather than a single-turn completion
- ✓Organise evaluation work and ownership across a product team
Highlights
- •Taught by Evidently's own co-founders, who maintain the open-source library the tutorials run on
- •Ten-plus tutorials that are almost all code; only one lecture-style session in the whole course
- •Notebooks are public in the evidentlyai/community-examples repo, so they outlive the email sequence
- •Covers adversarial testing and response safety, which most evaluation courses omit
- •Genuinely free: open-source library plus a free Evidently Cloud tier, with only LLM API calls costing anything
Who It’s For
Best For
- ✓AI and ML engineers putting evaluation around a production LLM feature
- ✓Data scientists building an LLM judge that has to survive review
- ✓Product and research folks who need to define what good means for an LLM app
Prerequisites
- •Basic Python to run the notebook tutorials
- •An LLM API key and a few dollars of request budget
- •An LLM application or use case you actually want to evaluate
FAQ
What is LLM Evaluation for Builders: Applied Course?
A free applied course from Evidently AI's co-founders, delivered as a three-week email series of 10+ video tutorials, each paired with an end-to-end code example. It covers deterministic checks, custom LLM judges tuned to human preference, RAG retrieval and generation quality, adversarial testing, agent evaluation, and how to divide evaluation work across a team.
Is LLM Evaluation for Builders: Applied Course free?
LLM Evaluation for Builders: Applied Course is free to access.
What level is LLM Evaluation for Builders: Applied Course for?
LLM Evaluation for Builders: Applied Course is aimed at a intermediate audience. Recommended background: Basic Python to run the notebook tutorials, An LLM API key and a few dollars of request budget, An LLM application or use case you actually want to evaluate.
How long does LLM Evaluation for Builders: Applied Course take?
Expect roughly ~3 weeks as an email series; 10+ video tutorials, self-paced. Most learners work through it at their own pace.
What will I learn from LLM Evaluation for Builders: Applied Course?
You'll learn: Layer deterministic validations and model-based scoring into one evaluation suite; Design a custom LLM judge and tune it to match human preferences; Choose between reference-based and reference-free evaluation for a given task; Measure retrieval quality and generation quality separately in a RAG system; Run adversarial tests to surface model vulnerabilities and assess response safety; Evaluate a basic AI agent rather than a single-turn completion; Organise evaluation work and ownership across a product team.
Topics
Sources
This page was written from 2 sources, 1 on domains other than evidentlyai.com.