Understanding how user prompts affect AI responses - what does this mean for brand visibility?

Michael, you’ve touched on something fundamental. Prompt structure significantly influences AI output.

The main prompt pattern categories:

Why different prompts = different recommendations:

AI systems interpret intent from prompt structure. “Best CRM for small business” triggers different training associations than “CRM for startups with small teams.”

The second is more specific, so AI:

• Looks for sources addressing that exact scenario
• Filters for solutions marketed to that segment
• May de-prioritize enterprise-focused options

Content strategy for prompt patterns:

The content-prompt alignment principle:

Your content structure should mirror common prompt structures.

Examples:

Prompt pattern: “Best X for Y” Content to create: “Best [Product Category] for [Use Case/Persona]: 2026 Guide”

Prompt pattern: “X vs Y” Content to create: “[Your Product] vs [Competitor]: Complete Comparison”

Prompt pattern: “How to [achieve outcome]” Content to create: “How to [Outcome] with [Your Product]: Step-by-Step Guide”

Why this works:

AI looks for content that directly answers the query. Content structured to match the query pattern is more likely to be cited.

Our approach:

For each product/service, we create content for the top 3 prompt patterns in our category. This ensures we have citable content regardless of how users phrase their queries.

User search behavior perspective:

How users actually phrase AI queries:

People query AI differently than they search Google. AI queries are:

• More conversational
• Longer (average 20+ words vs. 3-4 for Google)
• More context-rich
• Often include constraints (“under $50”, “for beginners”, “without coding”)

Common patterns in conversational queries:

1. “I’m looking for a [category] that [constraint]”
2. “What’s the best [product] if I [situation]”
3. “Can you recommend a [product] for [use case]”
4. “I need something that [capability] but also [constraint]”

Implication for content:

Your content should address specific constraints and situations, not just generic features. When users add constraints, AI looks for content addressing those constraints.

“Best project management software” ≠ “Best project management for remote creative teams under 20 people”

The second query needs content that specifically addresses remote, creative, small teams.

Technical perspective on prompt interpretation:

How AI parses prompts:

1. Intent classification - What type of query is this?
2. Entity extraction - What products/categories are mentioned?
3. Constraint identification - What requirements are stated?
4. Implicit context - What’s assumed but not stated?

Why phrasing changes results:

“Best CRM for small business” → Entities: CRM, small business “CRM for startups with small sales teams” → Entities: CRM, startups, small sales teams

The second has more specific entities. AI retrieves sources that address all entities.

For marketers:

Create content that explicitly addresses common entity combinations:

• Your product + use case
• Your product + persona
• Your product + constraint (budget, size, industry)
• Your product + problem

Each combination is a potential match for a user prompt.

Competitive analysis angle on prompts:

Discover what prompts mention competitors:

1. Systematically test prompt variations
2. Note which prompts mention which competitors
3. Identify gaps - prompts where you should appear but don’t

What we found for a client:

The insight:

They dominated their strongest use case but were invisible for others. We created targeted content for the gap areas.

Within 3 months, they started appearing for previously invisible prompt patterns.

Product marketing perspective on prompts:

The positioning-prompt connection:

Your product positioning determines which prompts you match.

If you position as: “Enterprise CRM for large sales teams” You’ll match: “CRM for enterprise”, “CRM for large teams” You won’t match: “CRM for startups”, “affordable CRM”

The dilemma:

Broad positioning = match more prompts but less specifically Narrow positioning = match fewer prompts but dominate them

Our strategy:

We have primary positioning (narrow, specific) and create content for adjacent prompt patterns we want to capture.

Core positioning: “CRM for agencies” Extended content: “CRM for marketing teams”, “CRM for service businesses”

This captures prompts beyond our core positioning without diluting our brand.

Monitoring perspective on prompt visibility:

How to track prompt pattern performance:

1. Define prompt categories relevant to your business
2. Create test prompt lists for each category
3. Track visibility across prompt variations
4. Identify patterns in where you appear vs. don’t

Our monitoring approach:

We track visibility across:

• 50 “best of” prompts
• 30 comparative prompts
• 40 problem-solving prompts
• 20 recommendation prompts

Weekly monitoring shows:

• Which patterns we dominate
• Which patterns we’re invisible
• How visibility changes over time

Tools like Am I Cited help automate this. You can set up prompt variations and track mentions automatically.

Practical optimization for prompt patterns:

Quick wins for prompt coverage:

1. Add FAQ sections with question formats that match prompts

   • “Is [Product] good for [use case]?” → Matches validation prompts

2. Create comparison pages for each major competitor

   • “[You] vs [Competitor]” → Matches comparative prompts

3. Use case landing pages for each persona

   • “[Product] for [Persona]” → Matches best-of prompts

4. How-to content for problems you solve

   • “How to [solve problem]” → Matches problem-solving prompts

The minimum prompt coverage:

At minimum, have content for:

• Best-of queries (category landing page)
• Top 3 competitor comparisons
• Top 3 use cases/personas
• Top 5 problems you solve

This covers the highest-frequency prompt patterns.

This thread fundamentally shaped how I think about AI visibility. Key insights:

Prompt patterns determine visibility: Different query structures trigger different sources and recommendations. We need to optimize for patterns, not just topics.

The main pattern categories:

1. Best-of (25%) - Need authoritative list presence
2. Problem-solving (35%) - Need how-to content
3. Recommendation (20%) - Need use case content
4. Comparative (15%) - Need comparison pages
5. Validation (5%) - Need reviews/testimonials

Content strategy: Create content that mirrors prompt structures:

• “[Product] vs [Competitor]” for comparative prompts
• “Best [Category] for [Use Case]” for best-of prompts
• “How to [Outcome] with [Product]” for problem-solving

Monitoring approach:

• Define relevant prompt variations
• Track visibility across patterns
• Identify gaps and create targeted content
• Monitor changes over time

Our action plan:

1. Map common prompt patterns in our category
2. Audit content coverage against patterns
3. Create content for uncovered high-value patterns
4. Set up monitoring for prompt-based visibility
5. Iterate based on data

The positioning-prompt connection is key. Our positioning determines which prompts we naturally match. Content extends our reach to adjacent prompts.

Thanks everyone for the research-backed insights.