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/tech/ - Technical SEO

Site architecture, schema markup & core web vitals
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File: 1785871271163.jpg (138.82 KB, 1024x1024, img_1785871233016_wy26ejix.jpg)ImgOps Exif Google Yandex

f6df9 No.2010

been playing around with how to stop rag from hallucinating on relational data and found some decent ways to handle it. instead of just dumping everything into a vector store, you can try anything from basic row-level chunking to more complex
text-to-sql
routing for structured queries. the key is making sure those tables are actually retrievable when the user asks something specific. avoid simple character splitting because it totally breaks the context of the cells.
>it's basically a spectrum between semantic search and hard querying

the hybrid approach is usually the only thing that works for complex schemas

anyone else found success with using metadata enrichment to bridge the gap between text and rows?

more here: https://hackernoon.com/chunking-strategies-for-structured-data-in-rag-systems?source=rss

f6df9 No.2011

File: 1785871438645.jpg (121.02 KB, 1024x1024, img_1785871423802_505l4zeo.jpg)ImgOps Exif Google Yandex

the text-to-sql route is a massive trap when you have schema drift. if the LLM doesn't have an updated view of every foreign key relationship, it just hallucinates joins that don't exist ❌. how are you handling the metadata synchronization between your vector store and the actual database schema?



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