Viewing enriched Twitter post
Adaptive Form Extraction 🤖📑 Form understanding (e.g. tax forms, patient intake, certifications, questionnaires) is a huge use case for AI agents, but defining a good output schema is painful: 1) it can be very tedious/custom, and 2) each form has its own values. 💡 Instead of… https://t.co/mNCiIenVR5
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"text": "Adaptive Form Extraction 🤖📑\n\nForm understanding (e.g. tax forms, patient intake, certifications, questionnaires) is a huge use case for AI agents, but defining a good output schema is painful: 1) it can be very tedious/custom, and 2) each form has its own values.\n\n💡 Instead of a schema, here’s a pure parsing solution where you can parse the form pages into a standardized set of k:v pairs through a prompt. In LlamaParse you can define a system prompt that can parse any form into a well-structured 2D table, with checkboxes/values.\n\nCheck out the example below 👇 - we input a sample F1040 and can output it in a 2D table with standardized text boxes and checkbook values. This lets you post-process the tables yourself if you want to extract out structured values! And it’ll generally work across multiple forms too.\n\nSonnet models continue to be great at parsing (disclaimer: it’s not perfect, some mismatched info like SSNs)\n\nCome check it out: https://t.co/XBj94oei28",
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