Structure-aware document question answering using Chunkless RAG
Build a retrieval system for long, organized documents that preserves the document's hierarchy and lets an AI agent navigate sections, subsections, tables, images, and references instead of retrieving disconnected text chunks by similarity.
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From IBM Technology — What Is Chunkless RAG? How Docling & AI Agents Navigate Documents at 02:00
Problem: Chunk-based RAG can separate headings from the text they introduce, detach tables from their explanations, and miss relationships between sections. Structure-aware retrieval preserves context and lets the system follow the document's organization.
For: Organizations building document-understanding systems for long, structured documents where precision and relationships between sections matter.
Examples
- A 200-page annual report: the agent can locate the accounting-policies section, inspect the revenue-recognition policy, and follow a reference to the section explaining why it changed.
Behind this: 12 build steps · 2 tools and how each is used · how to validate demand · 6 things the video never answers.
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