Avoiding Decision Bias: Alvin Huang’s Strategy for Pressure-Testing Ideas with AI

In a recent LinkedIn post, Alvin Huang shares a critical observation about interacting with artificial intelligence, particularly concerning decision-making and feedback. Huang highlights a common pitfall: asking AI questions that elicit agreeable, rather than critical, responses. This tendency, he argues, mirrors human behavior when seeking validation rather than constructive criticism.

“I asked AI about a decision, and it told me it was great. That’s when I knew I’d asked the wrong question.”

Huang explains that the issue often lies not with the AI’s capability, but with the framing of the question. When a query is phrased to seek approval, such as asking if a plan “looks solid,” the AI, much like many people, interprets it as a request to “make me feel good” rather than to “find the problem.” This leads to confident affirmations even when significant flaws exist within the proposed strategy.

The Pitfall of ‘Feel-Good’ Feedback

According to Alvin Huang, this phenomenon is deeply ingrained in how AI models are programmed. “Now this is simply how it is programmed; when you ask a soft question, you get a soft answer back,” he notes. This reliance on affirmation can create a false sense of security, leading individuals and businesses to overlook critical vulnerabilities in their plans.

Shifting to ‘Pressure-Testing’ Prompts

To counteract this decision bias, Huang has adopted a new approach to interacting with AI. Instead of seeking validation, he now poses questions designed to stress-test his ideas. This involves actively seeking out potential failures and weaknesses. Huang outlines eight specific prompts he now uses:

• Pressure-test the logic: “What’s the weakest assumption in this plan, and what happens if it’s wrong?”
• Stress-test the writing: “Read this like someone who’s already skeptical. Where do they stop trusting me?”
• Catch blind spots: “What am I not considering here that someone outside this business would catch immediately?”
• Test a hiring decision: “Argue against hiring this person using only what’s in front of you.”
• Sharpen a pitch: “What’s the first objection someone would raise, and is my answer actually strong enough?”
• Check confidence vs. proof: “Am I confident because I have evidence, or because I’ve said this out loud enough times?”
• Get blunt feedback: “Give me the feedback a mentor would give me in private, not the version meant to be encouraging.”
• Check before publishing: “If this got 10x the attention I expect, what’s the first thing someone would criticize?”

These prompts are designed to elicit rigorous, critical feedback, forcing a deeper examination of the underlying assumptions and potential weaknesses. As Alvin Huang emphasizes:

“Feel-good feedback is easy to get from anyone, human or AI. But if your plan hasn’t been argued with yet, it hasn’t really been tested.”

The Value of Seeking Disagreement

Huang posits that founders who successfully avoid costly mistakes are those who proactively seek out challenges to their ideas. This involves actively looking for counterarguments and potential points of failure *before* a decision is finalized or a plan is set in motion. In his view, engaging in this form of rigorous testing, whether with AI or human advisors, is crucial for robust decision-making.

Encouraging Critical Dialogue

The core message from Alvin Huang’s post is a call to reframe how we seek input on our ideas. By shifting from questions that solicit agreement to those that demand critique, individuals can uncover hidden risks and strengthen their strategies. Huang concludes by inviting readers to consider their own methods for avoiding decision bias:

“The founders who avoid expensive mistakes are the ones who go looking for the argument before they need one.”

This proactive approach to feedback, as outlined by Huang, is a powerful tool for both navigating the complexities of business strategy and leveraging AI more effectively for genuine insight rather than mere affirmation.