AI Prompting Is a Critical Thinking Skill (Not a Shortcut to Avoid It)

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AI is making experts less credible, not more.

Here's how it happens:

You use AI to create analysis. It looks polished. Professional. Comprehensive.

Then someone asks: "Walk me through how you reached this conclusion."

And you can't.

Not because you're wrong. But because you can't explain reasoning you didn't do yourself.

This is happening everywhere.

Experts presenting conclusions they can't defend. Making recommendations they can't justify. Sounding smart until someone digs deeper.

Meanwhile, other experts handle these moments differently.

They demonstrate sophisticated reasoning. They own their analysis. They show their work.

Same AI tools.
Same expertise level.
Completely different credibility.

The difference: Some use AI to think better. Others use it to avoid thinking.

And when you can't defend your own analysis, people notice.

The Problem: AI as Thinking Replacement

Here's what happens when experts use AI as a shortcut instead of an enhancement:

• They ask for complete solutions instead of analytical frameworks.

• They accept outputs without testing them against their domain knowledge.

• They skip the hard work of connecting insights to specific contexts.

The result?

• They can present conclusions but can't explain the reasoning.

• They're recommending things they don't fully understand.

Trust erodes quickly when you can't defend your own work. Colleagues notice when you struggle under scrutiny.

You become someone who produces analysis rather than someone who demonstrates expertise.

The Solution: AI as Thinking Enhancement

The experts who are thriving with AI have figured something out:

They use AI to become better at what they were already good at. More strategic in connecting insights to outcomes. More sophisticated in their reasoning. More compelling when explaining complex ideas.

Their expertise becomes more valuable, not less, because they're demonstrating enhanced judgment rather than just producing better-looking analysis.

A Framework for AI Prompting That Builds Expert Credibility

I’ve been watching experts who handle this well, and they all seem to do the same things.

They’ve figured out how to use AI in ways that make their expertise more obvious, not less.

Here’s the pattern I keep seeing:

E - Engage AI to Expand Your Expert Analysis
X - eXamine Domain-Specific Factors AI Might Miss
P - Probe Beyond Surface-Level AI Insights
E - Evaluate AI Outputs Against Your Experience
R - Refine Solutions Using Your Specialized Knowledge
T - Test Conclusions Against Real-World Constraints

Let’s get it…

E - Engage AI to Expand Your Expert Analysis

Instead of: "What's the best database architecture for this application?" 

Expert approach: "I'm designing a database architecture for a high-frequency trading platform. Based on my experience, low latency and fault tolerance are critical trade-offs. Help me systematically examine architectural patterns, including edge cases I might not have encountered in my previous implementations."

When questioned, you can walk through sophisticated reasoning that demonstrates both domain expertise and analytical rigor.

X - eXamine Domain-Specific Factors AI Might Miss

Example: "I'm recommending a migration from our legacy CRM system. Given the integration complexity I've observed with our financial reporting systems and the data quality issues I know exist from our 2019 consolidation, help me examine migration factors that wouldn't be obvious from standard system replacement approaches but are crucial for our specific technical environment."

You demonstrate deep system knowledge and technical foresight that positions you as uniquely qualified to guide this initiative.

P - Probe Beyond Surface-Level AI Insights

Example: "The standard recommendation for improving API performance is caching and load balancing. But given the data consistency requirements I know our financial transactions require and the compliance constraints our audit team has emphasized, what second and third-order effects should I examine? How do I optimize performance while maintaining the data integrity that our business model depends on?"

Your ability to see beyond obvious solutions demonstrates the kind of domain expertise that justifies consulting you rather than just using AI directly.

E - Evaluate AI Outputs Against Your Experience

Example: "This analysis suggests implementing microservices architecture across all our applications, but based on my experience with our team's deployment capabilities and the maintenance overhead I've observed in similar architectures, this approach may create more problems than it solves. Help me think through how to customize this recommendation based on what I know about our specific technical constraints and organizational capacity."

You show sophisticated understanding of implementation realities that only experienced domain experts would consider.

R - Refine Solutions Using Your Specialized Knowledge

Example: "This security framework makes sense in principle, but given the legacy authentication systems we're locked into and the regulatory requirements specific to our industry, I need to modify the implementation strategy. Help me think through these customizations while maintaining the framework's core security principles."

You demonstrate both technical knowledge and practical judgment that inspires confidence in your recommendations.

T - Test Conclusions Against Real-World Constraints

Example: "This technical architecture looks solid, but help me think through what could break during implementation. Based on my experience with similar systems, performance issues typically emerge under peak load conditions that testing doesn't replicate, and integration problems usually surface with edge-case data that we haven't seen yet. What failure scenarios should I prepare for?"

Your ability to anticipate and prepare for technical challenges demonstrates the kind of expert foresight that prevents costly mistakes.

POLL

In Case You Missed It!

• Grab your Pocket Guide to Impromptu Conversations
  (Essential questions and cues to connect quickly—without sounding rehearsed)

• Learn the formula for doing deep work in a world of distraction.

• Transform small talk into deeper connections with proven conversation strategies

The Bottom Line

The most successful experts I know aren't avoiding AI or using it for everything.

They've figured out how to use it to think better about complex problems.

While others are becoming dependent on AI answers, these experts are becoming irreplaceable - better at solving problems that require both systematic analysis and deep domain knowledge.

Thanks for reading. Be easy!
Girvin