LLM Evaluation for Builders: Applied Course
by Evidently AI
Ship evals for your LLM app: LLM judges, RAG evaluation, and adversarial tests, in runnable code.
Overview
'LLM evaluation for builders: applied course' is a free, code-first program from Evidently AI — the team behind the open-source Evidently evaluation and observability library. Across 10 end-to-end Python tutorials delivered over about three weeks, it walks through the practical LLM evaluation workflow: automated quality checks and model-based scoring, designing and tuning custom LLM judges, evaluating RAG systems on both retrieval and generation quality, adversarial testing for safety and vulnerabilities, and tracing outputs for tasks like summarization, classification, and content generation. Every lesson pairs short video with runnable code using the open-source Evidently library, so engineers leave with reusable eval harnesses rather than theory. A completion certificate is available.
At a Glance
- Topic
- Agentic
- Level
- Intermediate
- Format
- Course
- Cost
- Free
- Duration
- 10 code tutorials over ~3 weeks, self-paced
- Provider
- Evidently AI
- Hands-on
- Yes — code/exercises
- Certificate
- Available
What You’ll Learn
- ✓Build test datasets and compare prompts and models systematically
- ✓Design, tune, and validate LLM-as-a-judge evaluators
- ✓Evaluate RAG pipelines on retrieval and generation quality
- ✓Run adversarial and safety testing to surface failure modes
- ✓Trace and score LLM outputs for summarization, classification, and generation
- ✓Stand up repeatable eval workflows with the open-source Evidently library
Highlights
- •10 hands-on, end-to-end code tutorials — not slides
- •Taught by the maintainers of the open-source Evidently eval/observability tool
- •Covers the eval topics that actually break in production: judges, RAG quality, adversarial tests
- •Free, with an optional completion certificate
Who It’s For
Best For
- ✓AI/ML engineers building and shipping LLM or RAG applications
- ✓Teams replacing ad-hoc spot-checking with real evals
- ✓Technical PMs who need to reason about AI quality and observability
Prerequisites
- •Basic Python
- •Familiarity with calling LLM APIs / building a simple LLM app
FAQ
What is LLM Evaluation for Builders: Applied Course?
A free, applied course from Evidently AI teaching AI/ML engineers how to evaluate LLM and RAG applications with real code — building test datasets, tuning LLM-as-a-judge, and running adversarial safety tests. For anyone who works hands-on on LLM apps and needs a repeatable way to measure quality.
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, Familiarity with calling LLM APIs / building a simple LLM app.
How long does LLM Evaluation for Builders: Applied Course take?
Expect roughly 10 code tutorials over ~3 weeks, 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: Build test datasets and compare prompts and models systematically; Design, tune, and validate LLM-as-a-judge evaluators; Evaluate RAG pipelines on retrieval and generation quality; Run adversarial and safety testing to surface failure modes; Trace and score LLM outputs for summarization, classification, and generation; Stand up repeatable eval workflows with the open-source Evidently library.