LLM Evaluation Workshop
A two-day intensive on building eval datasets, golden sets, and regression pipelines that prevent silent LLM degradation.
Duration
2 days · 8-10 hours/week
Level
Intermediate
Delivery
Online
Status
Open for enrollment
Why this technology matters.
LLM evaluation is the practice of proving a model change helped: golden sets, eval datasets, scoring rubrics, model judges, and regression pipelines. It matters now because LLM quality degrades silently, and without evals teams discover regressions from user complaints instead of from tests.
It is used to build smoke and full regression suites that run on every prompt or model change, with human sampling for what automatic scores miss. It does not solve bad coverage: evals cannot catch failure modes absent from the dataset, model judges do not fix vague rubrics, and dashboards do not fix stale golden answers.
By the end the student will be able to build a 20-item golden set with approved answers, a three-level scoring rubric with worked examples, and a before-and-after regression run with an error analysis and a one-page findings note.
Why this course exists
The gap is between vibes-based prompt tweaking and evidence where every change is scored, ranked by failure impact, and gated before release. The workshop teaches the arc from golden sets and rubrics through judge calibration and regression to error analysis, compressing months of eval mistakes into two days.
Know exactly what you're signing up for.
Who is this for
Prerequisites
- Basic familiarity with large language models
- Comfort reading Python examples
- No previous evaluation experience required
Technologies & tools
Skills you'll gain
What you'll be able to do.
- Author a golden-set evaluation suite for your own LLM workflow.
- Wire CI to fail builds on regressions in groundedness and quality.
You will build.
Every project ships as HIGAET Practical Training / Experiential Learning — portfolio-ready work, not exercises.
- Project 01
Golden set builder
- Project 02
Scoring rubric exercise
- Project 03
Regression test run
- Capstone
LLM evaluation mini-suite with findings report
Speak the language first.
- Golden set
- A small fixed set of inputs with approved correct answers used as the reference for checking model outputs.
- Eval dataset
- A collection of test prompts and expected answers that represents the real tasks the model must handle.
- Scoring rubric
- A written checklist that defines what counts as correct, partial, or wrong so scoring stays consistent.
- Model judge
- A second model used to score outputs against a rubric when exact matching is too strict.
- Human review sampling
- Having people check a random subset of outputs to catch errors that automatic scores miss.
- Regression suite
- A repeatable set of eval tests run after every change to catch drops in quality.
- Error analysis
- Grouping failures by type and impact so fixes target the most harmful problems first.
Fix, check, and go deeper.
Troubleshooting & common mistakes
Scores vary between runs on the same outputs
Freeze the dataset, prompt version, and scoring settings, then rerun and compare only one change at a time.
Model judge disagrees with human reviewers
Calibrate the judge on 30 labeled examples, tighten the rubric wording, and add two scored examples per level.
Golden answers go stale after product changes
Review and re-approve the golden set each cycle, version it, and record what changed and why.
Regression run passes but users still report bad answers
Add the reported failing prompts to the eval set, label the error type, and expand coverage for that category.
Eval takes too long to run on every change
Split into a fast smoke set for every run and a full suite nightly, then track both scores over time.
Before you move on, you should be able to
- Build a 20-item golden set with approved answers
- Write a 3-level scoring rubric with examples
- Score one output set by hand and with a model judge
- Run a before-and-after regression on a small prompt change
- Sort 10 failures into error types ranked by impact
- Write a one-page findings note with scores and next actions
Start your application.
Share a few details and a HIGAET advisor will reach out within one business day with next steps.
Common questions
Ready to start LLM Evaluation Workshop?
A 2 days course — Workshops.