Learn LLM evaluation in a course built only for you. And actually retain it.
The discipline that separates AI products that improve from ones that quietly rot: golden sets, LLM-as-judge, regression gates, and eval-driven iteration.
Free to start · no account needed
Built around your goal, not a catalog
What your course could open with
lrnit interviews you first (what you know, why you’re learning, how you think), then generates every lesson for that. Sample lessons from a course like yours:
Measured, not watched
By the end, you’ll be able to
- Build an eval suite for any generative feature
- Calibrate automated judges against human judgment
- Wire quality regression checks into your deploy pipeline
What a capability check looks likebeforenow
Build a golden set+63
Calibrate an LLM judge+64
Gate quality in CI+67
Every lesson ends in assessment. Mastery is tracked per concept with spaced review, and the course adapts to what you actually retain. Finishing means a timed final exam and a verifiable certificate, not a completion badge.
“It felt like the course already knew what I did for a living.”
Questions people ask
- Who is this aimed at?
- Engineers shipping generative features who need a real evaluation discipline. Comfort with Python and running experiments is assumed.
- What does it actually cover?
- Golden sets, LLM-as-judge and its failure modes, calibration against human judgment, and regression gates that catch quality drops in CI before users do.
- Is it specific to one framework or provider?
- No. The discipline is model- and tool-agnostic, so it applies whether you use a hosted eval product or your own harness.
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Free to start · no account needed
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