Learn how to evaluate LLM prompts and outputs with evidence, run fair A/B tests to detect regressions, score structured and semantic responses, and select the right model by balancing quality, consistency, cost, and latency to make defensible shipping decisions.
Overview
Syllabus
- Course 1: Understanding LLM Evaluation
- Course 2: Evaluating LLM Prompts and Outputs
- Course 3: Comparing Prompts Fairly
- Course 4: Selecting LLM Models for Your Use Case
Courses
-
You'll learn how to evaluate LLM prompts with evidence instead of impressions: anchoring tests in product decisions, defining observable criteria, running multi-run checks to catch variation, choosing fair scoring metrics, and reading report data to recommend whether to ship, revise, or gather more cases.
-
You'll move past simple pass/fail string checks to evaluate complex LLM outputs: separate format, structure, and semantics; verify shapes and syntax with Regex Match and Valid JSON; evaluate meaning with an LLM judge when deterministic checks fall short; and build reusable prompt templates with named variables to test diverse inputs at scale.
-
Learn how to compare two prompt versions fairly by freezing evaluation conditions and eliminating confounds. You’ll run Prompt A vs. Prompt B in the simulator, evaluate overall and case-level results across repeated runs, and inspect outputs to decide whether to keep, revise, or revert a prompt revision.
-
You'll learn how to navigate model families and tiers, turn requirements into hard constraints versus tradeoffs, and run controlled evaluations across candidate models. By weighing task scores, run consistency, critical edge cases, cost, and latency, you will interpret multi-dimensional reports to make a defensible, evidence-based model choice.
