Relevancy

Every reference carries a relevance rating — how well that particular piece of material matches the question being asked.

Relevance
Excellent	80–100%
Very good	60–79%
Good	40–59%
Ok	20–39%
Low	Below 20%

How it is worked out

Relevance is about meaning rather than wording. Candidate material is found by semantic similarity — matching the sense of the question rather than its words — and then scored on how well each piece actually answers what was asked.

This is why a policy passage about “encryption of stored data” can rate highly against a question asking “is customer data encrypted at rest?”, despite sharing almost no vocabulary.

Treat relevance as a rough guide

Relevance tells you how close a piece of material looked to the question. It does not tell you whether the resulting answer is any good.

A high relevance score on a passage that turns out to be about a different system, or a low score on a passage that happens to contain exactly the right sentence, are both entirely possible. Take it with a pinch of salt.

For judging an answer, confidence is a much better indicator — it is assessed against the answer itself, not just the material behind it.

What it is useful for

Relevance is most useful when you are checking why an answer came out the way it did. Scanning the references and their ratings shows you what ResponseHub thought was pertinent, which usually explains an odd answer quickly — often it has latched onto a passage covering a related but different topic.

It also drives one piece of behaviour directly: a knowledge base item matching closely enough is used verbatim.

Next steps

• Confidence
• Verbatim answers