How is GPTcsv better than using ChatGPT or Claude?

ChatGPT and Claude have added good data analysis features, but they fall short in a few ways — especially when every row counts. That's why we created GPTcsv.

ChatGPT, Claude, and any LLM can generate either language or code. The data analysis features in ChatGPT and Claude generate Python code to work with large data sets. They process your dataset with that code and then generate language to describe the results.

As a result, LLMs often take shortcuts with larger datasets. They may not reference all the data, may stop due to token limits, and sometimes hallucinate good sounding answers even when the underlying analysis is incomplete. In addition, they run slowly with larger datasets making it harder to explore and iterate on your data.

For example, I recently analyzed user research data with ChatGPT. I wanted to understand how often people expressed concern about pricing. The resulting analysis sounded good, but when I looked at the Python code used to analyze the results, ChatGPT was using a simple keyword search for "pricing", "cost", "expensive" to identify relevant user research responses.

That is so 2019. The power of LLMs is that they can understand language much better than a simple bag of keywords.

GPTcsv runs the AI at the row level, not at the dataset level. As a result, you can tune your prompt for exactly what you are looking for. In addition, each row is processed with its own context window giving you the most accurate and detailed results. This approach allows us to use parallel processing to give you results fast — sometimes 100x faster than the equivalent analysis in ChatGPT.

GPTcsv has a free tier. You can upload a project, up to 1,000 rows of data, to give it a try for yourself.