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Business & incomeExperience excerpt · English

The goal is to find similar services where we charge less

… The goal is to find similar services where we charge less. In the past, our sales people would spend hours combing through those invoices. We wrote a prompt for GPT4, fed in our services and prices, and asked it to find services we could potentially replace as well as our profit margin. It took us a day to write this prompt. The results were outstanding and GPT4 gave accurate results. We even asked it to package it up in a PDF for us to send to the potential customers. On a Monday morning, we started on the prompt. By Tuesday morning, we got it working well enough that we were confident ship it to a few of our sales people to test. This will save our company hundreds of thousands each year and we can get back to the potential customer much faster than before - increasing the likelihood of a sale. If we had to program this like normal software, it'd probably take months to get it right with dedicated engineering resources to account for new invoice edge cases. …

Original discussionWhen Will the GenAI Bubble Burst?142 source comments · at import
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DA

That's a great use-case! But what about when GPT hallucinates - gets it wrong. What happens when you have sent a (potentially legally-binding) quote to your customers, and you have to back out of it? Humans make mistakes - whether directly, or indirectly through the software that we craft. But when the mistake happens, we can reason about it, understand why it happened, and fix the problem. Wouldn't it be embarrassing to not be in a position to reason about / explain a mistake to your customers caused by this "thing" we use that's been trained on all the random information available on the internet? Would that not reduce the value and standing of your business (which is ultimately about the people and expertise contained within) in the eyes of customers? As an engineer, technologist, and CTO I absolutely love what I've been able to craft using generative AI under direct supervision. But …

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DA

Thanks for the example and that sounds really solid cost savings and definitely agree with the trend that it is here to stay. For invoice parsing (various formats), are you just using GPT4V? When GPT4V initially came out, i benchmarked it against an out of the box invoice parser from Google Cloud ( https://cloud.google.com/document-ai ) on 16 documents and it was much better accuracy wise. For ex: i'd get results parsing 10,100 as 101100 (no comma). Curious if you saw problems like this in your pipeline or if its gotten much better since?

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