Document Processing in AI Agents: RAG vs. Chat Attachment Approaches

This section compares the use of Retrieval-Augmented Generation (RAG) agents and document attachments in chat for processing and analyzing documents. It outlines the strengths, limitations, and recommended use cases for each approach.

Use Case / Criteria	Attachment in Chat (upload document)	RAG in an Agent (Retrieval-Augmented Generation)
Document summary	✅ Yes, possible (AI reads the entire document)	❌ Not suitable (RAG does not process the whole doc)
Document synthesis	✅ Yes, possible	❌ Not suitable
Global analysis of a document	✅ Yes, possible	❌ Not suitable
Precise data extraction	✅ Yes, but only from the whole document	✅ Yes, if the answer is in part of the doc
Question about a specific part of the doc	❌ Limited if the doc is too large	✅ Ideal (targeted search in the doc)
Search in multiple documents	❌ Complex (must read everything)	✅ Ideal (indexing & multi-document retrieval)
Document size limit	❌ Yes, limited by the context window of the chat	✅ Much less limited (RAG indexes and splits docs)
Relevance for precise search	❌ Less relevant	✅ Highly relevant
Max size handled / Scalability	❌ Limited by the context window of the chosen AI (e.g., 32k tokens, 128k tokens, etc.)	✅ No real limit on the volume or number of documents (high scalability)
Recommended use case	Summary, synthesis, global analysis	Precise info extraction, Q&A, multi-documents
Example usage	“Summarize the attached document”	“Find the law article that mentions X”