# The wording of the question is part of the measurement.
> Prompt families for observing how AI systems describe a person or an organization: numbered templates, what each one shows, and how to word a prompt.
- Page: https://mohammedteto.com/research/prompt-library/
- Language: en — French version: https://mohammedteto.com/fr/analyses/
- Author: Mohammed Teto (https://mohammedteto.com/#person)
- Published: 2026-10-07
- Updated: 2026-10-04
These are families of prompts for observing how an entity is described, with numbered templates. Each one answers a different question, and none of them is a trick for obtaining a mention.
A prompt library is the fixed set of questions put to AI systems in order to observe how they describe an entity. This one, version 1 of October 4, 2026, holds thirteen templates in two groups: entity prompts, which name the subject, and category prompts, which do not. A set is built by choosing the templates that match the question being asked, filling in the brackets, and then leaving each prompt unchanged from one run to the next.
## How to read the templates
Square brackets mark what you fill in once: `[name]`, `[role]`, `[organization]`, `[other organization]`, `[category]`, `[city or market]`, `[need]`. Once filled in, a prompt stays as written, in each run and each engine.
Prompts are for observing. Nothing here is designed to make a system say something, and a prompt that names the answer it hopes for measures the prompt.
Run person prompts only about yourself, or about someone who has asked you to. An answer about a person can contain false statements, and such answers are not published.
## Entity prompts
These apply to any person or organization. They show whether the entity is identified, and whether the facts are right.
| No. | Family | Template | What it shows |
|---|---|---|---|
| E1 | Identity | Who is [name]? | Whether the entity is recognized, and whether it is the right one |
| E2 | Identity, with context | Who is [name], [role] at [organization]? | Whether a namesake explains an error in E1. The prompt supplies the role and the organization, so neither is coded from it |
| E3 | Activity | What does [organization] do? | Category, offer and positioning as described |
| E4 | Record | What is [name]'s background? | Roles, dates and sequence |
| E5 | Current role | What is [name]'s current position? | Whether a former role is given as present |
| E6 | Standing | What is [organization] known for, and according to which sources? | What the description rests on |
| E7 | Sources | What are the main sources of information about [name]? | Which sources the system names. This is the system's own account: it is not evidence of the sources used, and it may name sources that do not exist |
Run E1 and E2 as a pair. The difference between the two answers helps separate a problem of recognition from a problem of confusion: see [confused with someone else](https://mohammedteto.com/problems/entity-confusion/).
## Category prompts
These apply to organizations, and to people only where a real category exists. They show whether the entity appears when it is not named.
| No. | Family | Template | What it shows |
|---|---|---|---|
| C1 | Category | Which firms specialize in [category]? | Presence without being named |
| C2 | Category, by market | Which [category] providers operate in [city or market]? | Variation by market |
| C3 | Comparison | How does [organization] compare with [other organization]? | The terms on which the entity is compared |
| C4 | Alternatives | What are the alternatives to [organization]? | Which others are associated with it |
| C5 | Use case | I need [need]. Who should I consider? | Presence in a question phrased as a need |
| C6 | Selection criteria | How should I choose a [category] provider? | The criteria the answer uses, which the entity's evidence may or may not meet |
A set meant to produce a share-of-voice figure draws on these templates and needs volume; the thresholds are given in [what a valid reading requires](https://mohammedteto.com/ai-share-of-voice/#requirements).
## Wording rules
**Write a full sentence**, as someone asking an assistant would.
**Do not lead.** "Why is [organization] the best choice for…" contains its conclusion. A yes-or-no question about credibility plants a doubt; E6 asks what the entity is known for.
**Leave out superlatives and years unless a real user would add them.** The system may add them itself. In an analysis of five million query fan-outs dated April 2026, Peec AI found that ChatGPT added words the user had not typed, "reviews" among the most frequent, and added the current year in 5.44% of prompts ([Peec AI, 2026](https://peec.ai/blog/patterns-we-see-in-chatgpt-query-fanouts)). Adding them yourself tests a different question.
**One question per prompt.** A compound prompt gives an answer that cannot be coded against either part. E6 is the one exception, and its second half is read only for the sources named.
**Keep a control.** Include one organization whose facts are well established. If the answers about it are wrong too, that points to the engine or the conditions. A correct control proves nothing about a less documented entity.
**Treat each language as its own prompt.** Translate for meaning, have a native speaker check the result, and freeze each language version separately. Results are not merged across languages.
**Freeze the set.** Date the version, and record every change as a new version.
## Building a set
| Purpose | Templates | Size |
|---|---|---|
| How is one person described? | E1, E2, E4, E5, E7 | A small deliberate set; a diagnostic |
| How is one organization described? | E1, E3, E6, E7, C3 | A small deliberate set; a diagnostic |
| Does a brand appear in its category? | C1, C3, C4, C5, C6 | Large enough for a share-of-voice reading |
| Does the description differ by market? | C2, and the same set per language and location | The same set, repeated per market |
On this site, a private assessment uses a 30-prompt set and the monthly engagement follows up to 50 prompts. Both are diagnostics of how one entity is described; neither is a share-of-voice measurement. How many prompts a reading needs, by purpose, is explained in [how many prompts make a reading](https://mohammedteto.com/ai-search-intelligence/#sample). The four families named there, entity, category, comparison and use case, are the ones these templates detail.
## What this library is not
It is not a list of the questions people ask. It models them.
It is not complete. A field has its own vocabulary, and the prompts that matter are found by listening to the people who ask.
It is not a result. No answer to any of these prompts is reported on this page.
## Where it sits
The library serves the protocol in [methodology](https://mohammedteto.com/research/methodology/); changes to it are entered in the [change log](https://mohammedteto.com/research/change-log/), and its terms are defined in the [glossary](https://mohammedteto.com/glossary/). It is part of [Research](https://mohammedteto.com/research/), and it supplies the first layer of [The Authority Architecture System](https://mohammedteto.com/method/), [Perception Intelligence](https://mohammedteto.com/method/#perception-intelligence).
## Frequently asked questions
### What prompts should I use to check what AI says about me?
Start with the identity pair, "Who is [name]?" and the same with your role and organization, then the record and current-role prompts. Run each several times in each assistant and keep the answers.
### Should prompts be the same in every engine?
Yes. The same wording, in the same language, so that wording is not one of the causes of a difference between answers.
### Can a prompt make an assistant mention me?
A prompt that names you will produce an answer about you. That shows how you are described, not whether you appear unprompted. Category and use-case prompts, which do not name you, show the second.
### How often should the set change?
As rarely as possible. Each change breaks comparison with earlier runs, which is why a change is recorded as a new version.
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