Explore ChatGPT 4o and NLSQL strengths, limitations, and which tool is best suited for your company data analysis needs

In this blog post, we compare the data analytics capabilities of NLSQL and ChatGPT-4o using a dataset of import-export trade balance transactions from the US worldwide by product groups. We used an open dataset from the census, covering the years 2008-2023. Our test began by sending a full dataset as a CSV file to the latest ChatGPT-4o model and asking the question, "What are the top 5 products in export to China last year?"

ChatGPT-4o Response: The ChatGPT-4o response can be seen in the screenshot provided in this blog post. The top 5 products from highest export to lowest were:

1. Civilian aircrafts, engines, equipment, and parts
2. Semiconductors
3. Industrial Machines
4. Passenger cars
5. Crude Oil

NLSQL Response: NLSQL responded with a different set of top 5 products, which is actually correct and can be verified using the original CSV file filters. The top 5 products according to NLSQL were:

1. Soybeans
2. Crude Oil
3. Pharmaceutical preparations
4. Industrial machines
5. Civilian aircrafts, engines, equipment, and parts

The simple data structure used for testing both tools can be viewed in the provided screenshot. The history of questions for ChatGPT-4o is also available in the screenshot for reference.

Based on the results, NLSQL provided a more accurate analysis of the import-export trade balance dataset. To further explore the capabilities of both tools, you can check the ChatGPT-4o screenshot and try the NLSQL open demo using the link provided below. This will allow you to make your own tests and compare the performance of both tools for the same dataset.

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NLSQL Open Demo containing import-export trade balance transactions worldwide