The RAG ingestion layer problem: what it takes to build one
Source
enconvert.com
Author
het-dave
Date
Context
An AI system that searches documents can retrieve a passage successfully and still receive unusable information. EnConvert's engineering article describes how extraction can mix text across columns, strip table values of their row and column context windowThe maximum amount of text a model can consider at once — its working memory for the current conversation or task.Full definition →, or retain website navigation. Those mistakes enter the searchable collection before retrieval begins. Its proposed ingestion layer converts different source formats into one consistent structured format that keeps headings and tables and reports errors explicitly instead of returning empty text. The design principle is to check what enters the collection: adjusting search behavior cannot restore relationships that extraction has already discarded.
Terms in this piece · Glossary
context window — The maximum amount of text a model can consider at once — its working memory for the current conversation or task.
RAG — Retrieval-augmented generation — fetching relevant documents first and pasting them into the model's context so it answers from your data instead of memory.
