dedicated

Here is a huge trick to cut down on hallucinations: I have an agent that processes phone receipts using GPT-4o. Whenever I go to a restaurant or pay for something, I take a photo of the receipt and upload it to Google Drive. The agent goes through the folder and extracts the following information from each receipt: • Name of the place • Category of the place • Amount • Tax amount • Tip amount Each field requires an individual prompt. The result is a JSON object that I export to a separate folder. This is awesome, but there's a problem: The agent occasionally makes mistakes when interpreting a receipt; unfortunately, I couldn't detect them as they happened. Here is the trick: I'm now using a challenger model (o3-mini) to check the work of GPT-4o. This increases the cost of processing receipts but reduces the mistakes to almost nothing. The best part: 1. Whenever the challenger disagrees with the main model, I produce detailed information about the problem to improve the prompts and make the extraction more robust. 2. It's very easy to turn this process into a Human-in-the-Loop workflow where I get notifications of every disagreement and an opportunity to settle them. I recorded the attached video to show you how this works using @GetUnstract.