# About this booklet: report

Introduction of Decision AI, by Vishal Singh (NYU Stern). Generated from the chapter's data files; the reading version is at https://vishalsingh.org/decisionai/about-this-booklet. Table n of this report is figure n of the chapter.

## Identification

- Title: About this booklet
- Chapter: Introduction
- Question: What does this booklet cover, how are its numbers produced, and how should a reader use its interactive scenarios?
- Dek: 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.
- Grain: records
- Status: draft
- Published: 2026-10-29
- Updated: not revised
- Data sources:
  - Authored examples. This page describes the booklet and reports no data; weights: not applicable; no data are sampled
- Canonical URL: https://vishalsingh.org/decisionai/about-this-booklet
- Report: https://vishalsingh.org/decisionai/about-this-booklet/report
- Markdown: https://vishalsingh.org/decisionai/about-this-booklet/report.md
- BibTeX key: singh2026aboutthisbooklet

```bibtex
@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.}
}
```

## Principal estimates

The dek and the opening prose cite no fact.

## Findings

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](https://typesafe.ai/). 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](https://vishalsingh.org/decisionai/reference/reading-guide) explains the kinds of claims a chapter can make, and the [methods page](https://vishalsingh.org/decisionai/reference/methods) 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](https://vishalsingh.org/decisionai/reference/changelog) records each change.

Instructors can add `?mode=instructor` to a chapter's address to show teaching notes beside the sections they concern.

## Tables

The chapter has no figures.

## All estimates

The chapter has no facts.json.

## Definitions

The chapter marks no glossary term.

## 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.

## 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.

## References

The chapter cites no reference.

## Provenance

- Build script: decision_ai/chapters/about-this-booklet/build.py (sha256 7fb9b7091c511d4746eaf753d0a98213c5df7b4b1adfcc7ab4b77d41ae5e02b5)
- Built on: 2026-10-29
- Site commit: 252942a900a49d01fe9f278e18459eb87b20b4a0

Outputs:

| File | SHA-256 |
| --- | --- |
| facts.json | ca3d163bab055381827226140568f3bef7eaac187cebd76878e0b63e9e442356 |
| meta.json | 49afb3b4d16fc4197c82ec050aca93ff6217c238794f8e4a3e47ddc74076317b |
| refs.json | ca3d163bab055381827226140568f3bef7eaac187cebd76878e0b63e9e442356 |

## Machine formats

- Report (HTML): https://vishalsingh.org/decisionai/about-this-booklet/report
- Report (Markdown): https://vishalsingh.org/decisionai/about-this-booklet/report.md
- Every estimate (JSON): https://vishalsingh.org/decisionai/report/about-this-booklet/facts.json
- Index of every report: https://vishalsingh.org/decisionai/llms.txt; all reports in one file: https://vishalsingh.org/decisionai/llms-full.txt