How AI is Redefining Accuracy in Document Review

Why the need for LLM?

While searching for humidity data recently, I stumbled upon a fascinating document from the India Meteorological Department, Jaipur. Being data-curious, I naturally took a closer look at their weather forecast PDF. And guess what? I spotted a mistake!

Take a look yourself:

India Meteorological Department Weather Forecast

Original document can be found here

(See if you can spot the issue in the “JODHPUR::BALESAR” section of the forecast above)

Did you catch it? In the “JODHPUR::BALESAR” forecast, the Max Relative Humidity values (19, 7, 9, 6, 7) are consistently lower than the Min Relative Humidity values (50, 22, 38, 17, 15) for all five days! It seems like the “Max” and “Min” labels for Relative Humidity were accidentally swapped in this section.

It was a simple, yet telling, example of how easily human errors can creep into documents, even official ones, when they aren’t meticulously rechecked before publication. Think about it – in a real-world scenario, someone relying on this forecast might make incorrect assumptions about the expected humidity levels.

The solution

This is precisely where the power of Artificial Intelligence steps in to revolutionize document review. Large Language Models (LLMs), with their ability to understand context and identify anomalies, can be incredibly efficient at validating documents and ensuring accuracy before they reach the public eye.

Intrigued, I decided to put this to the test. I used a simple Python script (you can see a snippet in the image below!) to extract the text from the PDF document and then tasked two powerful AI agents with reviewing it: DeepSeek R1 and Qwen 32b reasoning model.

Download the code from here

And guess what? DeepSeek R1 nailed it! As you can see in the output, it correctly identified the swapped Max and Min Relative Humidity values as a key discrepancy, along with other formatting and consistency issues within the document. Qwen 32b also provided a review, but DeepSeek R1’s response was particularly precise and focused on the core logical error.

This little experiment powerfully illustrates the potential of AI in enhancing document accuracy and reducing the risk of human error. Imagine integrating an AI agent into your workflow to automatically review critical documents – reports, contracts, data sheets – before they are finalized and distributed. The implications for efficiency and accuracy are enormous!

If you’re wondering how an AI agent could become your secret weapon for avoiding errors and streamlining your document workflows, let’s connect! I’d be happy to discuss how AI-powered solutions can be tailored to solve your specific challenges and bring a new level of accuracy to your operations. Reach out, and let’s explore the possibilities together!