A developer encountered an issue where a large language model, when forced to adhere to a strict schema with all fields marked as required, hallucinated data. The model invented a delivery date for supplier emails that did not contain one, leading to logistical problems for a warehouse. The developer recommends making all schema fields nullable and adding a mechanism to verify that the extracted data actually exists in the source text to prevent such confident guessing. AI
IMPACT Highlights the importance of flexible schema design and data validation when using LLMs to prevent hallucinated outputs.
RANK_REASON Developer shares an experience and recommendation about LLM behavior with strict schemas.
AI-generated summary · Google Gemini · from 1 sources. How we write summaries →