Lanthos

Report / 6 chapters

The Eval Discipline Field Guide

A consolidated report on why most enterprise AI programs never produce a measured outcome, what a defensible eval looks like in production, and how to choose metrics that survive contact with real operations.

What's inside
  • Why adoption metrics quietly replace outcome metrics — and how to stop it
  • Baselines that can actually beat you, and how to set them before you build
  • The failure condition test: naming the losing number before the project starts
  • Why calibration data, not model choice, is the durable advantage
Chapters
  1. 1

    The eval discipline gap: why most enterprise AI never produces measured outcomes

    14 min read

  2. 2

    How to build an eval that survives production

    9 min read

  3. 3

    Grading the wrong exam

    5 min read

  4. 4

    Automation inherits the metric, not the goal

    11 min read

  5. 5

    The value of AI isn’t a number

    10 min read

  6. 6

    Calibration data is the moat

    12 min read

Written for: Heads of AI, transformation leads, and operating executives funding AI programs.