🧪Learn to Evaluate LLM Outputs Systematically
Move from eyeballing LLM outputs to running a CI eval that blocks regressions on a real prompt. You'll build a 20-item dataset, write a binary rubric, calibrate LLM-as-judge, and ship the harness in your repo.
Phase 1Why 'looks good' isn't an evaluation
See why vibes-based testing hides real regressions
Vibes are not an evaluation method
6 minVibes are not an evaluation method
False confidence and false alarms hurt differently
6 minFalse confidence and false alarms hurt differently
Anecdotes lie; distributions don't
7 minAnecdotes lie; distributions don't
Phase 2Building your first eval set and rubric
Build your first 20-item eval set and rubric
Collect twenty real inputs before you write any rubric
7 minCollect twenty real inputs before you write any rubric
Binary rubrics force the question 'what counts as success?'
7 minBinary rubrics force the question 'what counts as success?'
Write golden outputs to lock in your taste
7 minWrite golden outputs to lock in your taste
If two humans disagree, the rubric is broken — not the humans
7 minIf two humans disagree, the rubric is broken — not the humans
Phase 3Reference-free scoring and LLM-as-judge
Use LLM-as-judge and pairwise scoring without bias
When a teammate proposes 'just check if it sounds right'
7 minWhen a teammate proposes 'just check if it sounds right'
Your judge prefers verbose, formal answers — even when they're wrong
8 minYour judge prefers verbose, formal answers — even when they're wrong
The new prompt scores higher — but only on easy items
8 minThe new prompt scores higher — but only on easy items
Phase 4Shipping a CI eval for your prompt
Wire a CI eval that blocks regressions
Set up the eval harness as code, not a notebook
8 minSet up the eval harness as code, not a notebook
Add an LLM-as-judge layer with calibration
8 minAdd an LLM-as-judge layer with calibration
Wire the eval into CI so it runs on every PR
8 minWire the eval into CI so it runs on every PR
Document the eval and schedule the dataset refresh
8 minDocument the eval and schedule the dataset refresh
Frequently asked questions
- How do you evaluate LLM outputs without a ground-truth answer?
- This is covered in the “Learn to Evaluate LLM Outputs Systematically” learning path. Start with daily 5-minute micro-lessons that build from fundamentals to hands-on application.
- What makes LLM-as-judge biased and how do you correct for it?
- This is covered in the “Learn to Evaluate LLM Outputs Systematically” learning path. Start with daily 5-minute micro-lessons that build from fundamentals to hands-on application.
- How big does an eval set need to be to catch regressions?
- This is covered in the “Learn to Evaluate LLM Outputs Systematically” learning path. Start with daily 5-minute micro-lessons that build from fundamentals to hands-on application.
- What's the difference between offline evals and CI evals?
- This is covered in the “Learn to Evaluate LLM Outputs Systematically” learning path. Start with daily 5-minute micro-lessons that build from fundamentals to hands-on application.
- When should you use pairwise preference instead of a rubric?
- This is covered in the “Learn to Evaluate LLM Outputs Systematically” learning path. Start with daily 5-minute micro-lessons that build from fundamentals to hands-on application.
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