AI Harness Engineering Series — Book 2
"Uneval'd AI is
untrustworthy AI."
AI Harness Engineering Series
Make Your AI Evaluations
Reliable
The Problem
Most teams ship AI features and hope for the best. This book teaches you to measure — with eval harnesses, test datasets, and regression detection that tell you when something breaks before your users do.
Sound Familiar?
What's Inside
Chapter 1
A minimal eval pipeline running against a real prompt in under an hour.
Chapter 2
Curating inputs with expected outputs. Avoiding leakage. Keeping benchmarks honest over time.
Chapter 3
LLM-as-judge, rubrics, and custom scorers. Choosing the right scorer for your use case.
Chapter 4
Catching quality degradation when a model or prompt changes. Automated alerts vs human review thresholds.
Chapter 5
Routing ambiguous outputs for human review. Designing review interfaces. Closing the feedback loop.
Chapter 6
Aggregating scores into a dataset-level signal. Avoiding Goodhart's Law. Trend dashboards that tell you whether your eval suite is working.
"Uneval'd AI is untrustworthy AI."
— Chapter 1: Quick Start
"Your blind spots are in the cases you didn't write."
— Chapter 2: Test Datasets
"It didn't break on deploy. You just noticed there."
— Chapter 4: Regression Detection
"A metric that never changes is not measuring anything."
— Chapter 6: Metrics Collection
The Series
Book 2 — Available Now
This book · PDF · $25
Book 3 — Coming Soon
In progress
Book 4 — Coming Soon
Planned
6 chapters on building reliable AI evaluation harnesses. Working code, real scenarios, verification steps in every chapter.