What you will be able to show
A versioned evaluation suite that catches one real quality regression and documents the remaining uncertainty.
ai · Intermediate
LLM Evaluation & Reliability
Turn a promising model demo into a measurable system with task-specific datasets, graders, human review, regression gates, and production feedback.
Learn activelyShort decisions before explanation
Verify independentlyTests, sources, and failure cases
Finish with proofArtifacts another person can inspect
Skills practiced in context
Four-mission proof sprint
Do the work. Keep the evidence.
Check a mission only after the proof exists. Progress stays in this browser and also appears in the Modern Skills catalog.
-
01
Define observable success
Write a narrow task contract with useful, harmful, abstaining, and ambiguous outcomes.
Proof: A rubric with examples for every outcome class. -
02
Build the test distribution
Create representative, edge, adversarial, and previously failed cases without leaking answers into the input.
Proof: A versioned dataset of at least twenty labeled cases. -
03
Combine grading methods
Use deterministic checks where possible, model graders where justified, and human review for disputed cases.
Proof: A grader agreement report with known blind spots. -
04
Install a regression gate
Compare two system versions on quality, safety, latency, and cost, then define the release threshold.
Proof: A before-and-after scorecard that blocks one unacceptable regression.
0 of 4 proof missions complete
0%
Proof sprint complete
Explore $29 founding access
Want structured feedback and deeper practice?
The free path remains yours. Founding access helps shape future mock interviews, review rubrics, and portfolio verification.
Primary learning sources
Go deeper without entering an endless feed.
These external resources are optional benchmarks. Complete the LearnByShorts mission and bring back one useful artifact.