My colleague came by my desk last weekend and mentioned that he’d seen ChatGPT being used to detect fraud in invoice images. Apparently, it could identify small inconsistencies that suggested fraudulent activity. Since we work at an accounting software company, he thought it might be an opportunity worth exploring in our product.
Since the release of ChatGPT, I’ve heard similar ideas from many non-engineers. Essentially, they’re proposing what amounts to a text or image classifier: they have a prompt template where a document goes in and a classification comes out.
It’s interesting to watch non-engineers suddenly approach me with machine learning ideas, and I think it’s largely a byproduct of how easy it has become to use language models. The prompt-based interface has put these powerful tools directly into the hands of ordinary people, inspiring them to come up with Machine Learning (ML) classifier ideas.
Another interesting observation is that these non-ML engineers often don’t consider error margins when applying models to, say, a million documents. Many who are inexperienced with ML see a classification problem as straightforward, without an intuitive grasp of the edge cases and distributional shifts you may encounter at scale. They may not realize that classification models need to be robust to handle such complexity.
However, I really enjoy the increasing accessibility of this technology. In fact, I’m curious if there are any software products that let non-ML engineers tune text or image classifiers with prompts, while also guiding them toward good ML practices. If so, I believe it should be possible for non-ML engineers to produce high-quality classifiers all by themselves.