LLM Evals and Fine-tuning

Name: LLM Evals and Fine-tuning
Price: 89 USD
Availability: InStock

Most developers ship AI features and hope they work. The ones who build reliable AI products measure. This course teaches you to build eval pipelines that detect regressions before users do, score output quality with LLM-as-judge, build golden datasets that capture what good looks like, and fine-tune open-source models when prompting alone cannot get you there.

Prerequisites: Generative AI for Builders · Production AI Engineering

Start free lessons

$89one-time · lifetime access

What you'll learn

✓Why evals matter — the cost of untested AI features and what a real eval pipeline looks like

✓Building a golden dataset — how to capture what good looks like so regressions are caught automatically

✓LLM-as-judge scoring — using Claude to score outputs for quality, correctness, and tone at scale

✓Eval pipelines in code — running your production feature against a test set and reporting scores per run

✓Detecting regressions in CI — blocking deploys when eval scores drop below a defined threshold

✓When to fine-tune vs prompt — the decision framework, and what fine-tuning actually changes in model behaviour

✓Fine-tuning open-source models — preparing training data, running a fine-tune job, and evaluating the result

✓Production eval infrastructure — storing results, trending scores over time, and alerting on quality degradation

Course outline

Free — no account needed

Why Evals Matter

The cost of untested AI features — and what a real eval pipeline looks like

7 minFree

Building Golden Datasets

What to put in your test set, how many examples you need, and how to keep it current

8 minFree

Full course — $89 one-time

Eval Runners and Scoring

Automate running your test set and scoring outputs with exact match, regex, and heuristics

9 min

LLM-as-Judge

Use Claude to score Claude — quality evaluation that scales beyond what heuristics can measure

8 min

Evals in CI

Run evals on every PR — block merges when quality drops and track score trends over time

7 min

When to Fine-tune

The decision framework — when prompting fails and fine-tuning is actually the right answer

8 min

Fine-tuning in Practice

Prepare a dataset, run a fine-tuning job on OpenAI or Llama, and evaluate the result

10 min

Eval-Driven Improvement

The complete workflow — evals reveal weaknesses, you fix them, evals confirm improvement

8 min

Get the full course

8 lessons — from golden datasets and LLM-as-judge to CI regression detection and fine-tuning open-source models.

✓ 8 lessons✓ Measure before you ship✓ Certificate

$89one-time