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With Gemini selected in agent search, Rippit sends each of your questions to a Google Gemini model with Google Search grounding turned on, and saves the answer, the sources it cited, and the searches it ran. For how research tables work overall, see How research tables work.

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Setup

None. Gemini models are built into Rippit. When you turn on agent search, pick one or more Gemini models from the Search models list.

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How it works

  1. Write the brief. It sets the topic, intent, and audience for the questions.
  2. Rippit writes the questions. 30 per input by default, grouped by theme. You can read and edit them before running.
  3. Each question goes to Gemini as-is, with Google Search on and no extra instructions from Rippit.
  4. Every answer is saved as a row, and each question is repeated (twice by default) to show how consistent the answers are.
Select up to three models in one table to compare Gemini with ChatGPT or Claude on the same questions.

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What each row holds

One row per question, model, and repeat: the question and its theme, Gemini’s answer, the links it cited, the searches it ran, its grounding details (which parts of the answer each source supports), a full trace, and the status of the call. Failed calls are kept as rows too. Gemini cites sources through Google redirect links. Rippit keeps those links as Google returns them.

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Good to know

  • These are API calls with search on, not a recording of the Gemini app or Google’s AI Overviews. Answers can differ from what someone sees there.
  • A sample runs up to three questions on all selected models, once each.
  • Each new run is saved as its own study. The results show the latest, and older studies stay available in Agent search.

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Good fits

  • Seeing how Google’s AI describes your product and your competitors.
  • Comparing which sources Gemini grounds its answers in against ChatGPT and Claude.