After an AI model produces an answer, most workflows stop there. The analyst reads the output, makes a judgment call, and decides whether to trust it. That judgment happens in isolation: one person, one answer, no second perspective. The peer review turn is a built-in way to change that. It lets any answer in Qonera be routed to an independent AI model for structured critique before a human reviewer makes the sign-off decision.
After an AI answer appears in a Qonera chat, a review panel lets the user select which previous answers should be critiqued. Any answer in the conversation can be selected: a single-model answer, a multi-model synthesis, or a combination of both. The user then chooses which model will perform the review, and submits.
The reviewing model receives the original question, the selected answers with their attribution labels, and the source documents the conversation is working from. It produces a structured critique: which claims are well-supported, which are weak or unsupported, where the evidence is ambiguous, and what deserves closer attention before the answer is used. That critique streams live to the interface as it is generated, so the reviewer can start reading and forming a judgment while the review is still running.
The peer reviewer is not reading the original answer in isolation. It has access to the same evidence the first answer was grounded in, which means it can check citations against the source documents directly. If a claim in the original answer overstates what a source document actually says, the reviewer will often catch it. If a finding in the original answer is directly supported by a passage in an uploaded file, the reviewer can confirm that too. This is what separates a structured peer review from asking the same question to a second chatbot in a separate window: the reviewing model is reviewing text plus its underlying evidence, not text in isolation.
Every peer review turn is attributed to the model that ran it. The attribution header in the review output names the reviewing model explicitly, so there is no ambiguity about which model produced which critique and which answer it reviewed. When a multi-model synthesis is being reviewed, the header shows the full synthesis attribution so the reviewer knows exactly what is being assessed. When individual answers are selected, each one is named in the review.
The peer turn is also recorded in the tamper-evident audit trail, alongside the original answer. The audit record captures which review model ran, the timestamp, token counts, and the full chain from question through to sign-off. That record does not require any additional effort from the team: it is created automatically as part of the workflow, not assembled separately after the fact.
The peer review turn fits best when you have a specific answer you want challenged before it goes out. It is the right tool when the multi-model stress test flagged a set of claims as uncertain and you want a focused critique of those areas specifically. It also works well as a final check on high-stakes outputs: a synthesis that looks broadly correct but includes one section that reads thin can be peer-reviewed on its own, without running the full question again.
It is also useful for answers that came from outside Qonera. A draft produced in another tool, a section of a document written by a colleague, or a summary generated elsewhere can be pasted into a Qonera conversation and peer-reviewed directly with any source documents attached. The reviewer sees the content and the evidence together, which produces a more grounded critique than a model reading the text alone.
For teams running multi-model stress testing as their default mode for client-facing work, the peer review turn adds depth after the stress test has run. The stress test produces a synthesis with a Conflict Heatmap that shows where claims are uncertain. A peer review turn targeted at those specific claims gives the reviewer a named second read before the sign-off step. Both the synthesis and the peer turn are part of the same chat, in the same audit trail, and visible to whoever reviews the work later.
For teams working in single-model mode for internal drafts and iterative work, the peer turn is the structured upgrade from reading an AI answer alone. Rather than a single analyst deciding whether to trust an answer, a named reviewer model adds a second perspective before the human makes the final call. The comparison between the original answer and the critique is visible in the chat, not scattered across different conversations or tools.
See the full review workflow at qonera.ai/how-it-works, or explore plans.
Multi-model stress testing, Conflict Heatmap, tamper-evident audit trail, and structured sign-off, built for teams who need defensible AI output.