Should marketers learn prompt engineering to understand how users ask AI questions?

Good question. Let me distinguish between different types of prompt knowledge:

Prompt engineering (technical):

• Crafting complex prompts for AI outputs
• System prompts, chain-of-thought, etc.
• Primarily for AI application building

Prompt understanding (marketing):

• How users naturally ask AI questions
• Query patterns and intent
• What triggers AI to cite sources

What marketers actually need:

You need prompt UNDERSTANDING, not deep prompt ENGINEERING.

Key differences between AI queries and search keywords:

The skill to develop:

Understanding conversational query patterns, not technical prompt crafting.

Content strategist perspective on prompt patterns:

How I use prompt understanding:

I test prompts before creating content. Here’s my process:

1. Identify topic - What do we want to rank for?

2. Test prompt variations

   • “What is [topic]?”
   • “How does [topic] work?”
   • “What’s the best [topic] for [use case]?”
   • “Compare [topic] options”

3. Analyze AI responses

   • What sources get cited?
   • What’s missing from answers?
   • What questions don’t get good answers?

4. Create content targeting gaps

   • Answer questions AI struggles with
   • Provide depth where AI is shallow
   • Create citation-worthy content

Example:

Tested: “What’s the best project management tool for remote teams?”

Found: AI cited general comparison sites but lacked specific remote-work feature analysis.

Created: Detailed guide on remote-specific PM features with comparison table.

Result: Now getting cited for remote team PM queries.

The prompt testing approach:

Use AI like your customers would. Create content that answers what they ask.

The evolution from keywords to prompts:

Keyword research (traditional SEO):

• Search volume data
• Competition metrics
• Keyword difficulty scores
• Monthly search trends

Prompt research (AI SEO):

• Query pattern analysis
• Citation triggering analysis
• Response gap identification
• Conversational intent mapping

What transfers:

• Understanding user intent
• Competitive analysis
• Gap identification
• Content planning

What’s new:

• Conversational query structure
• Multi-part questions
• Context-dependent answers
• Citation-based success metrics

My take:

The SKILLS transfer from keyword research to prompt research. The TOOLS and data sources are different.

A good keyword researcher can become a good prompt researcher with practice.

Data perspective on AI query patterns:

What we’ve learned from analyzing 50,000 AI queries:

Query length distribution:

• Average Google search: 3.5 words
• Average ChatGPT query: 18 words
• Average Perplexity query: 23 words

Query structure patterns:

• 62% are full questions (Who/What/How/Why)
• 23% are commands (Explain/Compare/List)
• 15% are keyword-style (carry over from Google behavior)

Intent complexity:

• 48% of AI queries contain multiple intents
• “What is X and how do I use it for Y?” = definition + application
• Traditional search separates these; AI users combine them

Implication for content:

Create content that:

• Answers complete questions, not just topics
• Addresses multiple related intents
• Uses natural language headers matching query patterns

Customer-facing perspective:

What I’ve learned talking to customers about their AI usage:

Customers use AI for:

• “I need to understand this quickly” (learning)
• “Help me compare options” (deciding)
• “I’m stuck, what should I do?” (problem-solving)
• “Can you explain this to my boss?” (communicating)

How they phrase questions:

They talk to AI like a smart colleague:

• “I’m trying to set up [product] integration with Salesforce but getting errors. What should I check?”
• “My team is debating between [product A] and [product B]. What are the key differences for a marketing team of 10?”

What this means for content:

Your content should sound like answers to colleague questions, not marketing material.

Natural, helpful, specific - like a knowledgeable teammate would respond.

The skill translation:

If you’re good at customer conversations, you’ll be good at prompt understanding.

AI queries = How customers naturally ask questions.

This discussion has clarified what skills actually matter.

My takeaways:

1. Prompt UNDERSTANDING > Prompt ENGINEERING - Marketing needs query pattern knowledge, not technical AI skills

2. AI queries are conversational - Full questions, longer, more specific than keywords

3. Testing is essential - Spend time actually using AI like customers do

4. Customer insight transfers - Understanding customer questions = understanding prompts

5. Content should answer natural questions - Not keyword-stuffed, but conversationally helpful

Skills I’ll develop:

1. Regular AI query testing (30 min/week)
2. Citation analysis (what prompts trigger citations)
3. Customer question mining (support tickets, conversations)
4. Conversational content writing

Tools I’ll use:

• ChatGPT/Perplexity for manual testing
• Am I Cited for citation monitoring
• Customer support data for query patterns

The mindset shift:

Stop thinking “keywords to rank for.” Start thinking “questions customers ask AI.”

Thanks for the guidance everyone!