Capabilities: RAG & Retrieval

This section becomes important when “just putting documents into context” stops being enough. The goal is not to force one architecture. The goal is to help you answer a more practical question: how far does retrieval need to go before answers become reliably grounded for your use case?

Choose a path by problem complexity

You only need relevant passages

Start with basic RAG. It is the right first move when the problem is simply finding the right supporting content and letting the model assemble the answer.

Retrieved chunks lose too much context

If the chunks you retrieve are often technically relevant but hard to interpret on their own, move into contextual retrieval. The point is not more complexity for its own sake. The point is making retrieved content easier to use correctly.

The problem is about relationships, not similar passages

When the task becomes “who is connected to whom” or “how do these entities relate,” knowledge graphs often make more sense than continuing to scale plain vector retrieval.

Recall and precision keep fighting each other

If you already retrieve enough material but ranking is noisy or unstable, it is time to think about hybrid retrieval, reranking, and evaluation.

What this section helps you do

add traceable grounding to model answers
move retrieval from “works sometimes” toward “works more consistently”
decide when to deepen RAG and when to switch to a more structured representation
build a base for citations, evaluation, and quality regression