LLM Evals and Fine-tuning
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.
What you'll learn
Course outline
Free — no account needed
Full course — $89 one-time
Eval Runners and Scoring
Automate running your test set and scoring outputs with exact match, regex, and heuristics
LLM-as-Judge
Use Claude to score Claude — quality evaluation that scales beyond what heuristics can measure
Evals in CI
Run evals on every PR — block merges when quality drops and track score trends over time
When to Fine-tune
The decision framework — when prompting fails and fine-tuning is actually the right answer
Fine-tuning in Practice
Prepare a dataset, run a fine-tuning job on OpenAI or Llama, and evaluate the result
Eval-Driven Improvement
The complete workflow — evals reveal weaknesses, you fix them, evals confirm improvement
Get the full course
8 lessons — from golden datasets and LLM-as-judge to CI regression detection and fine-tuning open-source models.
Written by the RadarTrek editorial team · Reviewed June 2026
About this course
As AI applications move to production, two skills become critical: evaluating whether your model is performing well (evals) and customising model behaviour for your specific task (fine-tuning). This course covers both: how to design and run LLM evaluations that catch quality regressions before users do, and when and how to fine-tune a language model on your own data to achieve behaviour that prompting alone cannot produce.
LLM evals and fine-tuning are the skills that take AI engineering from "I can call an API" to "I can maintain quality at scale and customise models for specific domains." After completing this course you will be able to build an evaluation suite for your AI features, run evaluations in CI/CD pipelines, and successfully fine-tune a small language model for a classification or generation task.
Frequently asked questions
What makes a good LLM evaluation?
A good eval is: representative of your actual use cases, automated so it can run in CI/CD without human review for every run, measuring the right things (task completion, accuracy, format correctness, safety), and calibrated — the eval score should correlate with user satisfaction. The hardest part is designing test cases that catch real failure modes rather than trivial edge cases.
When should I fine-tune instead of just prompt engineering?
Fine-tuning makes sense when: you need consistent output format or style that is hard to enforce with prompts, you have high-quality labelled data of your desired input/output pairs, the task is well-defined and repetitive, and the quality or cost difference justifies the effort. Prompt engineering should always be exhausted first — it is faster and cheaper. Fine-tuning is for the gap between the best prompt and required performance.
How much data do I need to fine-tune an LLM?
Modern fine-tuning using techniques like LoRA and QLoRA requires far less data than training from scratch. For a well-defined task (classification, format consistency, domain terminology), 100–1000 high-quality examples can produce meaningful improvement. The quality of examples matters more than quantity — 100 perfect examples outperform 1000 noisy ones.
What is the difference between RLHF and supervised fine-tuning?
Supervised fine-tuning (SFT) trains the model to produce specific outputs given specific inputs, using pairs of input and desired output. RLHF (Reinforcement Learning from Human Feedback) trains the model using human preference judgments — humans rate which of two outputs they prefer. RLHF produces more nuanced alignment but requires more infrastructure. Most application-level fine-tuning uses SFT, which this course focuses on.
How do I run evals in a CI/CD pipeline?
Evals in CI/CD work like test suites — they run on every commit and block deployment if quality drops below a threshold. The pipeline: run your eval set against the new prompt or model, compute scores, compare to baseline, fail the build if regression is detected. Tools like Langsmith, Braintrust, and Promptfoo provide CI/CD integration. This course covers setting up an automated eval pipeline.