Source link : https://tech365.info/most-rag-programs-dont-perceive-subtle-paperwork-they-shred-them/
By now, many enterprises have deployed some type of RAG. The promise is seductive: index your PDFs, join an LLM and immediately democratize your company information.
However for industries depending on heavy engineering, the fact has been underwhelming. Engineers ask particular questions on infrastructure, and the bot hallucinates.
The failure isn’t within the LLM. The failure is within the preprocessing.
Normal RAG pipelines deal with paperwork as flat strings of textual content. They use “fixed-size chunking” (chopping a doc each 500 characters). This works for prose, however it destroys the logic of technical manuals. It slices tables in half, severs captions from pictures, and ignores the visible hierarchy of the web page.
Bettering RAG reliability isn’t about shopping for a much bigger mannequin; it’s about fixing the “dark data” downside by way of semantic chunking and multimodal textualization.
Right here is the architectural framework for constructing a RAG system that may really learn a handbook.
The fallacy of fixed-size chunking
In a typical Python RAG tutorial, you break up textual content by character depend. In an enterprise PDF, that is disastrous.
If a security specification desk spans 1,000 tokens, and your chunk dimension is 500, you’ve gotten simply break up the “voltage limit” header from the “240V” worth. The vector database shops them individually. When a person asks, “What is the voltage limit?”, the retrieval system finds the header however…
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Author : tech365
Publish date : 2026-01-31 22:05:00
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