Introduction
About this booklet
This booklet explores the decision endpoints that major AI labs have recently released, an interface popularized by TypeSafe's Jev, by posing business and workflow decisions as typed questions and checking whether acting on the answers beats the simplest credible alternative.
Full report: every estimate with its interval, and every figure as a table.
DraftFigures and numbers may change as the analysis is rebuilt.what is still being checked
First draft, updated as chapters open.
This booklet is for people who build or buy systems that act on a model's judgment: MBA students who have studied classification, and practitioners deciding whether a decision model, a language model, a trained classifier or a written rule should sit behind an action. Its subject is the decision endpoints that labs such as OpenAI and Perplexity now offer, an interface popularized by TypeSafe's Jev. It asks one question in every chapter. When does a model's judgment improve the action that a person or a system takes?
How a chapter is built
A case chapter starts from one decision with an owner and a fixed set of actions: contact a customer or not, route a complaint or escalate it, send a code change to a specialist, let an agent stop or make it continue. It states what evidence exists when the decision is made and what establishes a correct answer. It poses the judgment as typed questions, compares complete policies built on the answers, and prices them with costs that the reader can change. Where the data allow, a chapter shows a failure a reader can inspect, and each ends with what its data cannot show.
The comparison always includes the simplest credible alternative, such as a keyword rule, a trained classifier or a fixed step limit. When that alternative matches the model, the chapter says so, because a tie is a finding about whether the model is worth its cost.
How to read the numbers
Every statistic in a chapter is printed from that chapter's data file. Hover over a number, or focus it with the keyboard, to see its unit, population, period, source and interval. Costs in a scenario are assumptions and carry the tag "assumed"; quantities computed from the data carry the tag "measured". The reading guide explains the kinds of claims a chapter can make, and the methods page gives the rules every chapter shares.
No reader needs an API key. Every call to a model was made once and stored, so a chapter's numbers do not change when an engine does. Each chapter's methods name the engines and when they were called.
What is finished
All seven chapters are drafts, revised after a review of their metrics on October 30, 2026. Their labels are records that already exist, such as star ratings, company responses and task graders, and no one rated anything by hand. The changelog records each change.
Instructors can add ?mode=instructor to a chapter's address to show teaching notes beside the sections they concern.
What these data cannot show
This page describes how the booklet is built and how to read it. It reports no result, so it cannot show whether any engine or policy performs well.
Data sources
- Authored examplesThis page describes the booklet and reports no dataWeights: not applicable; no data are sampled · License: Authored for this booklet
Methods
This page reports no estimate. The rules every chapter shares are on the methods page.
Where the shared rules are
Samples, weights, intervals, cross-fitting, refusals and the caching of engine calls are described once on the methods page, and each chapter's methods note gives its own sample, engines and run dates.
How to cite
@misc{singh2026aboutthisbooklet,
author = {Singh, Vishal},
title = {About this booklet},
year = {2026},
url = {https://vishalsingh.org/decisionai/about-this-booklet},
note = {In Decision AI. Published 29 October 2026.}
}