I work with Retrieval Augmented Generation, or RAG, almost every day at GPT-Lab, Tampere University. It is a method where a system looks up information from documents and then uses a language model to answer a question. On paper, it sounds simple.
But when I started using it with long documents, I saw a problem. The answers were correct but not complete. They often missed the real details. That problem is what I call context coherence.
What is Context Coherence in RAG?
Context coherence means keeping answers clear, connected, and meaningful across the whole document.
Short documents usually work fine. But longer ones are tricky. The first pages often give a quick definition. Later parts usually explain things in more depth. RAG often grabs only the short line at the start and ignores the longer and more useful explanation.
So, the answer looks fine on the surface, but the real value is missing.
Example
Take a paper on machine learning.
At the start, it says: “Machine learning is a method of teaching computers to learn from data.”
Later, it says: “Machine learning includes supervised, unsupervised, and reinforcement learning. Each has algorithms and applications in areas such as vision, language, and robotics.”
Now, if you ask: “What is machine learning?”
RAG often picks the first line. The answer is not wrong. But it feels too short. What most people want is the longer explanation that comes later. That is where context coherence fails.
Why Context Coherence Matters
At first, this may not seem like a big issue. But it matters more than it seems.
- When answers are shallow, people lose trust.
- When details are missing, the system feels unreliable.
- In areas like research, law, or healthcare, an incomplete answer is not enough.
Context coherence is about more than accuracy. It is about telling the full story.
RAG is powerful. It helps keep answers fresh without retraining models. But if answers lose context, people stop trusting the system.
The real challenge is not only to fetch text. It is to keep the meaning whole. With better ways of splitting text, ranking passages, and guiding the system, RAG can give answers that are both clear and complete.
“Context coherence turns RAG from useful into reliable.”
About the author
