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MITHUNAI answers a question in four steps. It retrieves the passages of your content most related to the question, assesses whether that evidence is strong enough, generates an answer from those passages, and returns it with a citation for each claim. When the evidence does not support an answer, the assistant abstains and says the content does not cover the question, rather than answering anyway. Each step is described below, with the terms you will see in the console and the API.

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Retrieval

When a question arrives, MITHUNAI searches the knowledge attached to the assistant for the passages most related to it. The search is hybrid: a semantic search finds passages that mean the same thing even when they use different words, and a keyword search finds exact terms such as error codes, configuration names and file names. The two rankings are combined. Retrieval only ever searches the assistant’s own knowledge, inside your organisation. Nothing another organisation has ingested can be retrieved, whatever the question says.

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Grounding

The model is given the retrieved passages and asked to answer from them. MITHUNAI does not rely on that instruction alone. It assesses whether the evidence is strong enough to support an answer, and an answer the evidence does not support is not given. This is why an assistant’s instruction can change tone, format and audience, but cannot tell it to answer from general knowledge. Grounding is enforced by the platform, not by a setting.

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Citations

Every answer carries citations. Each one points at a passage that was actually retrieved, with: Citations are built from the retrieved evidence, not written by the model, so they cannot point at a document that was never retrieved.
Show citations to your users. Letting a reader verify an answer is most of what makes it trustworthy.

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Abstention

When your content does not support an answer, the assistant declines and says so. In the API this is a successful answer with abstained: true and no citations. Treat an abstention as a correct result:
  • Show it to the user as a normal reply, not as an error.
  • Do not retry it automatically, and do not fall back to an ungrounded model call.
  • Use it as a signal. The unanswered questions report lists what your users asked that your content does not cover.
An assistant that answers everything cannot be trusted on anything. Abstention is what makes the other answers worth reading.

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Conversations and turns

A conversation is an ongoing exchange with one assistant. Each question and its answer is a turn. Conversations are stored, so you can list, rename, archive and delete them. A conversation handles one turn at a time. Asking a second question while the first is still being answered is refused with 409 conflict; wait for the first turn to finish, or start another conversation. Every turn ends in exactly one of three ways:

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Streaming

Answers can be delivered all at once or streamed as they are written, using server-sent events. Both deliver the same answer. See Streaming.

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Where to go from here

Retrieval can only find what your content says clearly, so the largest lever on answer quality is the content itself: see content best practices. If you are weighing up whether the grounding really holds — whether it can fall back to the model’s own knowledge, whether your content is used for training, whether another customer’s data can reach your answers — those are answered in the frequently asked questions. If an assistant is abstaining on questions your content does cover, troubleshooting works through the causes in order.
Last modified on September 27, 2026