Is Your AI Bluffing?

How to Recognize When Your AI Needs More Context

10 year old Benjamin asked an AI to help write a book report about Abraham Lincoln. The AI responded with an thoughtful analysis: "Lincoln's pragmatic approach to political coalition-building demonstrated his profound understanding of antebellum electoral dynamics. His rhetorical prowess, particularly evident in his Cooper Union address, exemplified his ability to synthesize complex constitutional arguments into compelling public discourse." Benjamin stared at his screen, wondering if he should even copy and paste those sentences into his fifth-grade report.

Benjamin submitted the report not understanding the words and unsure how much of it is true or not. AI readily provides authoritative responses while missing crucial contextual information. Getting an answer was not the problem - recognizing the answer needed more context is.

The Warning Signs

Watch for the Generic Response. When an AI provides answers that could apply to any situation, it's likely operating without sufficient context. It's the difference between "This document discusses various business strategies" and "This Q3 report highlights a 23% revenue drop in European markets."

Beware the Perfect Answer. When an AI provides solutions without acknowledging complexity or potential challenges, it's a red flag. Real-world situations rarely have clean, universal solutions. If it sounds too straightforward, it probably is.

Notice the Static Solution. If the AI doesn't consider changing variables or environmental factors, it's working with incomplete context. Good answers acknowledge that different circumstances might require different approaches.

I intended to use the clock as an exercise of successfully using context to create an image I would use later…

… it became a great example of a Static Solution.

The Document Analysis Test Case

Consider asking an AI to analyze a quarterly financial report. A context-poor response might say: "The company performed well, showing growth in key areas."

A context-rich analysis would specify: "While North American software sales grew 15%, the European hardware division saw significant declines, suggesting regional strategy adjustments may be needed."

The difference? The second response demonstrates understanding of specific metrics, regional variations, and business implications. It's not just summarizing - it's showing comprehension of the document's nuances

When analyzing this stock, MXL (MaxLinear), I provided ample context. In addition I gave permission for the AI to estimate numbers from the financial report. The result was a satisfactory response to a complex question.

Your Context Checklist

Before trusting an AI's response, ask:

• Can the AI provide specific examples from the source material?

• Does it acknowledge limitations or uncertainties?

• Can it explain how different factors might affect its conclusions?

• Did it ask a clarifying question?

Building Better Context

Here's what good context-building looks like in practice:

Bad: "Analyze this report."

Better: "This is our Q3 financial report. Focus on European market performance and compare it to Q2. Flag any significant changes in our software division."

Bad: "What's the main point?"

Better: "What are the three biggest risks identified in the executive summary, and how do they relate to our previous quarter's challenges?"

Making It Work

The key isn't to abandon AI when answers are unsatisfactory and shallow - it's to iteratively build context. Start broad, then narrow down. Ask for specific examples. Challenge assumptions. Use the AI's responses to guide your next questions.

Remember: AI is a tool for augmenting your analysis, not replacing it.

When you spot these warning signs, it's not a failure - it's an opportunity to build better context and get more valuable insights.