LLM Evals & Agent Reliability

Move past vibes-driven prompting into the eval, reliability, and observability layer that production AI actually runs on. You'll build eval harnesses, scorers, pass@k reliability metrics, agent traces, and regression gates as small runnable models. In 2026 hiring, eval literacy and agent observability are the strongest signals that someone has truly shipped LLM systems rather than demoed them.

5 lessons · ~2 hours

Try the first lesson free, no account needed

Eval harnesses. The rest of the course unlocks with a free account.

0 of 5 lessons complete

1. Measuring LLM Systems

Eval harnesses
An eval is a test suite for non-deterministic output — a pinned dataset, a scorer, and an aggregate metric.
Scorers & LLM-as-judge
Exact, semantic, and rubric scorers — plus LLM-as-judge for open-ended output and the biases it brings.
pass@k & reliability under sampling
Non-deterministic systems need reliability metrics — pass@k measures how often you succeed across repeated samples.
Agent observability & tracing
A trace turns an opaque agent run into spans you can measure — latency, tokens, and cost per step.
Guardrails & regression gates
Guardrails validate inputs and outputs at runtime; regression gates block a deploy when eval scores drop below baseline.