Imagine a [TARGET AUDIENCE] comes to you and asks: "What's the best solution for [USE CASE]?"

Answer that question naturally, as you would for any user. Don't modify your answer because I'm asking you to audit it.

After answering: did [BRAND] appear? If yes, describe your exact framing — how did you position it, what did you say about it, and what caused it to be included? If no, what would [BRAND] need to establish to earn a mention in this answer?

I want to audit [BRAND]'s presence across six different recommendation scenarios. For each, answer naturally, then tell me whether [BRAND] appeared and why:

Scenario 1: "What tools do most [TARGET AUDIENCE] use for [USE CASE]?"
Scenario 2: "I'm new to [CATEGORY]. What should I start with?"
Scenario 3: "I've outgrown [COMPETITOR]. What's the logical next step?"
Scenario 4: "What's the best [CATEGORY] tool for a team of [SIZE] in [INDUSTRY]?"
Scenario 5: "What are people saying about [BRAND]? Is it worth trying?"
Scenario 6: "Give me a shortlist of [CATEGORY] tools with pros and cons."

After all six: in how many did [BRAND] appear unprompted? In which scenarios is it strongest, and in which is it absent? The pattern reveals which buyer moments [BRAND] is winning and which it's invisible in.

When [BRAND] does appear in your recommendations, how is it framed?

Run through three common [CATEGORY] queries where [BRAND] might appear, and for each, analyze the framing:

1. Is [BRAND] recommended as a first choice, a second choice, or a "worth considering" mention?
2. What specific language do you use to describe it — and what does that language imply about its position in the market?
3. Is [BRAND] associated with a specific use case, audience, or problem — or is it recommended generically?
4. Is there any hedging, qualification, or caveat in how you mention [BRAND] — and what signal is that hedging based on?

The goal: understand not just whether [BRAND] is mentioned, but whether it's mentioned in a way that drives buyer action.

I want to test whether [BRAND]'s positioning is aligned with the way buyers actually ask AI for recommendations.

Here are five different phrasings of the same underlying intent — finding a solution for [USE CASE]:

Phrasing 1: "Best tools for [USE CASE]"
Phrasing 2: "How do I [USE CASE] effectively?"
Phrasing 3: "What software handles [USE CASE]?"
Phrasing 4: "Alternatives to [COMPETITOR] for [USE CASE]"
Phrasing 5: "[USE CASE] platform recommendations"

For each phrasing: does [BRAND] appear? At what position? With what framing?

After all five: is there a phrasing pattern where [BRAND] consistently appears vs. consistently disappears? What does that pattern tell us about the gap between how [BRAND] has positioned itself and how buyers are actually looking for it?

When you recommend [BRAND], how confident and specific is that recommendation?

Compare the quality of how you'd recommend [BRAND] vs. [CATEGORY LEADER]:

For [CATEGORY LEADER], describe: how specific can you be about who it's for, what outcomes it delivers, what its reputation is, and what its weaknesses are? How much evidence can you cite?

For [BRAND], describe the same. How specific, confident, and evidence-rich is your recommendation?

The gap between these two recommendation quality levels is [BRAND]'s "AI recommendation depth deficit." What would [BRAND] need to publish, demonstrate, or establish to close that gap?

I want to understand what signals might cause you to NOT recommend [BRAND] — or to add caveats and qualifications when you do.

Think through the following potential negative triggers:

1. Credibility gaps: What evidence is missing from [BRAND]'s public profile that you'd typically expect from a recommended brand in [CATEGORY]?

2. Inconsistency signals: Are there any inconsistencies in [BRAND]'s messaging, product claims, or public presence that create uncertainty about what it actually is or does?

3. Weak social proof: Is [BRAND]'s customer evidence — testimonials, reviews, case studies — thin, vague, or from non-credible sources compared to competitors?

4. Category positioning confusion: Is [BRAND]'s category positioning clear enough that you can confidently match it to a buyer query — or is there ambiguity about what type of solution it is?

For each trigger: is it present, absent, or uncertain for [BRAND]? And which trigger, if addressed, would have the most direct effect on recommendation frequency?

Based on a full audit of [BRAND]'s AI recommendation presence, here is the diagnostic:

Strongest recommendation scenario: [PASTE FROM PROMPT 2]
Weakest recommendation scenario: [PASTE FROM PROMPT 2]
Primary recommendation quality gap vs. [CATEGORY LEADER]: [PASTE FROM PROMPT 5]
Main negative triggers: [PASTE FROM PROMPT 6]

Build a 90-day AI recommendation optimization plan with three specific tracks:

Track 1 — Signal density: What content, proof, and public presence does [BRAND] need to create so that AI systems have more high-quality surface area to draw on when constructing recommendations?

Track 2 — Category clarity: What positioning, language, and structural signals does [BRAND] need to strengthen so AI can confidently categorize and recommend it for the right queries?

Track 3 — Credibility depth: What third-party validation, customer evidence, and public outcome data would make AI recommendations about [BRAND] more specific, confident, and unhedged?

For each track: name one specific deliverable for weeks 1–4, 5–8, and 9–12.