Large language models are powerful tools, but they are not oracles. They generate text based on patterns, not understanding. This means they can produce highly confident, well-written responses that are completely wrong. To use AI effectively in insurance, you must shift your mindset from accepting its output to actively challenging it. This is the core of the Skeptical Inquiry Framework.

Think of the AI as a new, incredibly fast junior analyst. It can draft summaries, find clauses, and organize information in seconds. But like any junior analyst, its work requires rigorous review by an expert—you. Your experience, intuition, and deep policy knowledge are the essential filters that turn the AI's raw output into reliable insight.

AI-generated inaccuracies often come disguised in a polished package. The grammar is perfect, the tone is professional, and the structure is logical. This deceptive combination of high polish and low accuracy is what we call 'workslop'.

Workslop is dangerous because it looks finished. For example, when reviewing a First Notice of Loss (FNOL) report for a water damage claim, an AI might generate a clean summary:

*"The insured reports a pipe burst in the upstairs bathroom, causing water damage to the ceiling of the family room below. The incident occurred on May 15th at approximately 3:00 PM."

This looks correct. But a skeptical professional would ask: What kind of pipe? Was it a sudden failure or a slow leak over time? The AI summary omits the nuance needed to check for policy exclusions related to long-term seepage or faulty maintenance. Relying on the workslop without digging deeper could lead to a critical coverage mistake.

To find and fix workslop, you need to actively stress-test the AI's conclusions. Two effective techniques are adversarial prompting and role-based validation.

Imagine you've asked an AI to summarize the key liability exclusions in a general liability policy. It gives you a standard list. Now, you apply adversarial prompting.

This approach forces the model to re-examine the source material from a different perspective, often uncovering details it missed in its initial, more general summary.

Role-based validation takes this a step further. You instruct the AI to adopt a persona with a specific, critical point of view.

By framing the request this way, you guide the AI to apply a layer of critical analysis it wouldn't use by default. It moves from being a summarizer to a preliminary analysis partner, helping you spot the very workslop it may have just created.

Ultimately, the distinction between an automated suggestion and expert judgment is crucial. The AI provides the former; you provide the latter. The Skeptical Inquiry Framework ensures you never confuse the two. Every AI-generated summary, claim, or data point is a starting point for your analysis, not the conclusion. Your expertise is what shapes these preliminary outputs into a final, defensible decision.