This article details a method for organizing training and nutrition data within Obsidian using Large Language Models (LLMs). It explores what information is lost when LLMs convert free-form notes into structured tables, referencing 360 queries across three models. The piece also touches upon prompt injection vulnerabilities and the capabilities of a free Obsidian plugin called Veynrel. AI
IMPACT This exploration of LLM data conversion highlights potential challenges in structured data generation from unstructured text, impacting how users manage personal information.
RANK_REASON The article discusses a specific application of LLMs with a note-taking software, focusing on a plugin and data conversion process.
Read on Mastodon — mastodon.social →
AI-generated summary · Google Gemini · from 1 sources. How we write summaries →