Skip to content

Grounding and "I don't know"

Why the agent refuses to guess, how to trace an answer back to its sources, and what to do when it falls back too often.

Every answer is grounded: it is written from passages retrieved out of your knowledge base, not from the model's general training. When retrieval finds nothing relevant, the agent says so instead of producing something plausible.

The fallback is a feature

A support agent that invents a refund window is worse than one that says it does not know. The fallback is deterministic — no model call is made — so it cannot drift into a confident guess, and it always reads the same:

Set your own wording in the chatbot's fallback message, and point it somewhere useful — a contact form, a phone number, or a nudge to ask for a human. See Chatbot settings.

Tracing an answer

Open the Playground and turn on Debug. For each answer you get the retrieved chunks and their scores, the latency, the token usage, and the assembled prompt. That tells you which of two very different problems you have:

Sources are attached to each message as metadata rather than printed as [1] citations, which is why you read them in Debug rather than in the answer text.

When it falls back too often

In rough order of how often it is the cause:

  1. 1
    The content genuinely is not there. Check the

    knowledge gaps list — it is exactly the set of questions that got no sources.

  2. 2
    The content is there but unreadable. A scanned PDF or a JavaScript-only

    page extracts to nothing. Inspect the chunks — see Reindexing and freshness.

  3. 3
    The source is still processing, or disabled. Check its status.
  4. 4
    The similarity threshold is too high. Move it in small steps and re-test.

Resist fixing a fallback by loosening the threshold far: you trade "I don't know" for confident answers built from loosely related passages, which is the failure mode grounding exists to prevent.

Where to go next

Last updated