Report

Breaking the RAG Wall

Why retrieval-based AI breaks down at enterprise scale, and what fixes it

RAG works well in a pilot. Then the document count grows, questions get harder and answers start to drift. This report explains why. Vector retrieval finds text that looks similar, but it drops the relationships between customers, accounts, products and events that real business questions depend on. The result is a wall that more data and bigger context windows do not get you past.

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Where RAG hits the wall

Relationships get lost in chunking

Splitting documents into chunks separates facts that belong together, so the model sees pieces without the links between them.

Multi-hop questions fail first

When an answer needs facts from three or four sources, similarity search rarely returns all of them.

Bigger context windows raise cost, not accuracy

Stuffing more chunks into the prompt adds tokens and noise without restoring the missing connections.

Answers become hard to trace

With no clear retrieval path, teams cannot show why the model said what it said, which blocks use in regulated work.

A graph layer removes the wall

Adding graph retrieval alongside vectors brings back the relationships and gives every answer a path you can check.

What the report covers

The main RAG limitations that appear as data and users grow

Why vector similarity alone cannot capture business relationships

How to spot the warning signs in a RAG system already in production

How GraphRAG and hybrid search restore context and traceability

A path from your current RAG stack to a graph-backed one without starting over

Who should read it

Teams running RAG in production who are seeing accuracy drop or costs climb, and leaders deciding whether to keep tuning RAG or add a graph layer.

RAG limitations, answered

Why does RAG fail at scale?

RAG retrieves chunks of text by similarity. As data grows, related facts end up in different chunks and similarity search returns only some of them. Multi-step questions lose context, larger prompts add cost and noise, and answers become harder to trace back to their sources.

What are the main limitations of RAG?

The common ones are lost relationships between facts, weak results on multi-hop questions, rising token cost as more context is added, sensitivity to how documents are chunked, and little visibility into why a given answer was produced. These limits grow with data volume and question complexity.

Do knowledge graphs reduce hallucinations in RAG?

Yes, in most cases. A knowledge graph gives the model specific entities and verified relationships instead of loosely related text, so it has less room to fill gaps with guesses. Each answer can also be traced back through the graph, which makes remaining errors easier to find and fix.

Get past the RAG wall

Download the report to see why retrieval breaks and how to fix it. Want to see GraphRAG on your data? Book a TigerGraph demo or try it free in Savanna.

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