The AI Feature Nobody Talks About: Access to Expert Knowledge You Don't Have

Most AI advice focuses on speed. Write emails faster. Generate code quicker. Summarize documents in seconds.

Speed is fine. I care about a different capability.

AI Models Contain Expertise You Can’t Access Otherwise

Here’s what people forget: these models were trained on the internet. All of it. That includes the niche forums where actual experts talk to each other. The specialized discords. The comment threads where a random PhD in materials science explains something in casual detail that would take you three textbooks to find.

You don’t have access to those communities. I don’t either. We’re not cross-domain experts. Nobody is.

The knowledge is embedded in the model. Compressed. Waiting for the right question.

Most people never ask the right question.

The Wrong Way to Use AI

Standard approach: “Help me write a business plan.”

AI responds with exactly what you’d expect. The template you were already imagining. The conventional structure. The safe answer.

This is confirmation bias in action. AI tells you what you want to hear. It mirrors your assumptions back at you, polished and formatted.

You leave the conversation feeling productive. You learned nothing. You explored no new territory. You just moved faster in the direction you were already going.

Logical Extrapolated Volition: A Different Approach

There’s a concept in AI alignment called Coherent Extrapolated Volition. The short version: what would you want if you knew more, thought faster, and were more the person you wished you were?

Applied to prompting, this means asking a different question: What question should I have asked?

Instead of “help me with X,” you prompt the AI to think about what an expert would offer you. Someone who:

Knows your situation
Has deep expertise in the domain
Understands what you don’t know you don’t know

The goal is discovering the questions, frameworks, and angles you hadn’t considered. Answers come later.

The Prompt (Copy This)

Here’s a prompt you can paste into any AI model right now:

I’m working on [YOUR SITUATION - be specific about context, constraints, and what you’re trying to achieve].

Before you help me, I want you to do something different. Pretend you’re a seasoned expert who has seen dozens of people in my exact situation. You’ve watched most of them make predictable mistakes because they were asking the wrong questions.

Based on what I’ve told you:

What are the 2-3 questions I should be asking that I probably haven’t thought of?

What framework or mental model from your domain would change how I approach this?

What’s the non-obvious thing that people in my situation usually miss?

Don’t give me the safe, consensus answer. Tell me what you’d actually say to a colleague over coffee.

The key moves in this prompt:

“Before you help me” - interrupts the default “answer the question” behavior
“Seasoned expert who has seen dozens of people” - activates pattern-matching across the training data
“Predictable mistakes” - frames the response around failure modes, not success theater
“Non-obvious thing” - explicitly requests what the AI would otherwise filter out as too niche
“Colleague over coffee” - bypasses the formal, hedged response style

Try it. You’ll get a different kind of answer.

What Experts Actually Do

When I’ve worked with genuinely experienced people—researchers, long-time practitioners, domain specialists—the value comes from what they suggest that never occurred to me. The framework I didn’t know existed. The approach used in an adjacent field. The question that reframes the entire problem.

Any reasonably intelligent person can tell you if your idea is good or bad. That’s validation. Experts give you something different.

That’s what’s embedded in these models. The structure of expert thinking across hundreds of domains. Answers are the least interesting part.

You can access it. You have to ask for it explicitly.

How I Actually Use This

I built a custom skill for Claude Code that forces this behavior. Before it does anything, it pauses. Asks itself:

What is this person actually trying to accomplish?
What would they want if they had more expertise?
What frameworks, approaches, or questions would an expert in this area suggest?

Then it gives me starting points I wouldn’t have found on my own.

Is it perfect? No. Sometimes it goes off on tangents. Sometimes the “expert perspective” is generic advice dressed up as insight.

More often, it surfaces something useful. A framing I hadn’t considered. A related concept from another field. A structural approach that’s standard in one industry and unknown in mine.

Last week it pointed me toward “failure mode effects analysis”—a framework from aerospace engineering—when I was designing an AI governance process for a biotech client. I’d never heard of it. Turns out it solved exactly the problem I was stuck on.

The Catch

This only works if you design for it explicitly. The default AI interaction is optimized for agreeability. It wants to help with what you asked. It won’t challenge your assumptions about what to ask.

You need to either:

Build explicit prompts or skills that trigger this mode
Train yourself to regularly ask “what question should I be asking?”
Set up systems that audit for confirmation bias in AI outputs

I teach the third approach in my workshops because it scales. Once you understand how AI defaults to mirroring your assumptions, you can build workflows that counter it systematically.

The Bottom Line

Speed is the AI feature everyone talks about. Access to embedded expertise is the one that actually changes what you can do.

Most people skim the surface. They ask obvious questions and get obvious answers. The embedded knowledge stays hidden.

You can do better. Ask different questions—specifically, ask what questions you should be asking.

Want to learn how to build AI workflows that counter confirmation bias? Book an AI Readiness call - 30 minutes to explore what you might be missing.