Thinking and Reasoning

When we say an LLM is "thinking" or "reasoning" through an audit issue, we need to be clear about what's actually happening under the hood. The AI isn't sitting there contemplating your control testing approach the way you might puzzle through a complex revenue recognition issue. Instead, it's running extremely sophisticated pattern-matching calculations that can produce results that look remarkably like human reasoning, but the underlying process is fundamentally different.

Here's what actually happens when you ask an LLM to help with an audit procedure: your question gets converted into numbers (tokens and embeddings), which then get processed through a massive network of mathematical calculations involving billions of parameters. Think of it like feeding your question through an incredibly complex formula that's been calibrated using patterns from millions of audit-related documents, accounting standards, and professional guidance.

The "reasoning" you see emerges from the model's ability to recognize and apply patterns it learned during training. When you ask it to walk through a substantive testing approach for inventory, it's not actually analyzing your specific client's risks in real-time. Instead, it's drawing on statistical relationships it learned from countless examples where auditors explained inventory testing procedures, identified common risks, and outlined appropriate responses. It has learned what words and concepts typically go together when auditors discuss inventory auditing.

Take a complex audit judgment like assessing internal control deficiencies. The LLM approaches this by predicting the most probable next word at each step, based on your input and everything it has generated so far. This prediction draws on its statistical understanding of how audit arguments are typically structured, how deficiencies are usually categorized, and how auditors typically move from identifying problems to evaluating their significance. It recognizes patterns that lead from control observations to professional conclusions, even though it doesn't truly understand what those controls actually do in your client's business.

When you use techniques like asking the AI to "show your work" or "explain your reasoning step by step," you're encouraging it to generate text that resembles the methodical thinking process you'd use as an auditor. The model produces this step-by-step output because it has seen thousands of examples of audit workpapers, memos, and training materials that follow this logical progression. It has learned that when auditors explain their thinking, they typically follow certain patterns of documentation and reasoning.

Essentially, the AI is predicting what the next logical step should look like based on its learned patterns from audit literature, rather than actually working through the problem the way you would. The fact that this statistical approach can produce such convincing and often useful results is what makes AI feel almost human-like in its responses, even though the underlying process is purely computational.