What Your RAG System Isn’t Telling You
How complex documents expose the gaps in basic RAG—and how knowledge graphs can help.
Scale Company
An answer can sound right and still be missing something
Think of a RAG system as a new employee. RAG is a way for AI to find information in documents before it answers a question.
You ask the employee to read several long documents and answer a few questions. They give you an answer that sounds good. They even show you where they found it.
But they miss an important detail on another page. Their answer may be partly right. It just does not tell the whole story.
Example: A long contract
You have a 100-page contract, changes to the contract, and notes from your team. You ask:
“What happens if we miss the second deadline?”
The answer could be spread across several places:
| Where you look | What you find |
|---|---|
| The original contract | You must pay a fee. |
| A later change | You may get more time. |
| Another section | Extra time is allowed only in certain cases. |
You need all three pieces to get the full answer. If the AI finds only the page about the fee, it could give you the wrong idea.
Finding matching words is only the first step
A word search might find pages that say “second deadline.” A tool called vector search can also find text with a similar meaning, even if it uses different words.
This is useful when the answer is in one place or a few short sections. But long documents can be harder. The AI may need to:
- Follow clues from one page to another.
- Put facts together from several documents.
- Read many sections to find the main risks.
Finding a few good matches does not always find every piece of the answer.
A knowledge graph helps connect the clues
Think of a knowledge graph as a map. The map connects people, dates, rules, and other facts.
For our contract, it could show that:
- The second deadline is tied to a fee.
- A later change gives you more time.
- A rule explains when that extra time applies.
These links can help the AI follow the clues and find more of the information it needs.
The goal is to find the full story
| Tool | How it helps |
|---|---|
| Vector search | Finds useful pieces of information. |
| Knowledge graph | Shows how those pieces connect. |
Using both can help the AI give a more complete answer.
Complex documents? Let’s build AI that can handle them.
Want AI to answer questions about your documents, but worried it might miss important details?
Scale Company can help you build a RAG system tailored to your documents, business rules, and needs.