You’ve probably asked ChatGPT to write something, calculate something, or summarize a report. Sometimes it delivers exactly what you need. Other times, it gives you output that sounds plausible but completely misses the mark.
So what’s it actually doing?
For finance professionals, understanding how these tools generate output helps you make better use of them and spot the risks before they show up in a variance memo or board report.
What LLMs Are Actually Doing
Large Language Models generate responses by predicting one word or token at a time based on patterns they’ve seen in training. They aren’t searching a database or applying a rules engine. They’re using statistical modeling to determine what’s most likely to come next.
That applies whether the model is summarizing a 10-K, writing Python to analyze working capital, or generating commentary for a forecast variance. It’s all token prediction, applied across different contexts.
Because these models have been trained on a massive volume of text (think: the whole internet, including technical documentation, financial disclosures, and spreadsheet formulas) they can work fluently across both language and logic. With a clear prompt and structured input, they’ll generate a clean narrative or working code with equal ease.
They don’t understand finance, but they’ve seen enough examples to reproduce its patterns reliably.
Why They Sometimes Get It Wrong
LLMs are optimized for fluency, not accuracy. That means they can produce confident explanations even when the underlying data doesn’t support the conclusion. This is what’s known as hallucination, and it often shows up when prompts are too vague, data is incomplete, or outputs aren’t reviewed.
In a finance context, that might look like mislabeling a revenue category, generating incorrect percentage changes, or confidently citing a made-up reason for a shift in gross margin.
LLMs work best when paired with structured inputs and a skeptical eye.
The problem isn’t that the model is trying to deceive you. The problem is that it will always produce an answer, whether it has the right information or not.
This is why review, validation, and oversight still matter. Ask the model to explain its logic. Cross-check the output. And when possible, use tools that allow you to inspect the generated code or visualize the data being analyzed.
Where This Fits in a Finance Workflow
LLMs work best as an interface layer between your team and your data. They don’t replace your models or dashboards, but they can reduce the friction involved in getting answers from them.
You can upload budget vs actuals and ask for a variance summary with commentary. You can simulate changes to pricing, headcount, or COGS and ask the model to walk through the impact. You can explore your GL, forecast, or operational metrics without writing SQL or waiting on a custom report.
This makes analysis more accessible to more people, without lowering the bar for quality or control. It also creates new opportunities to automate repeatable tasks like board commentary, trend narratives, or audit documentation; without hardcoding every rule in advance.
The Bottom Line
LLMs generate language based on probability, not knowledge. But with the right inputs and oversight, they can handle real analysis, surface key drivers, and produce clear narratives.
In a finance setting, they’re most useful when integrated into existing workflows — working alongside your data, your models, and your people. The more clearly you understand how they operate, the more effectively you can use them.
Want the full implementation guide?
The Pro edition includes structured prompts, an LLM vs ML explainer, and a compliance-ready checklist for using these tools safely.