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Run a first Mira study by narrowing the research question, writing a guide with clear probing rules, recruiting participants with relevant experience, and testing the participant flow before launch. Mira is Decode by Entropik’s AI moderator for qualitative interviews; it can ask follow-ups within the scope set by your guide, but you remain responsible for the study design and interpretation.
What an AI-moderated interview can—and cannot—do
An AI-moderated interview is a qualitative conversation in which an AI system follows a discussion guide and asks follow-up questions based on participants’ answers. It offers more room for probing than a fixed survey and can conduct sessions in parallel, but its questions and adaptations are constrained by the guide and configuration.
Decode describes Mira as an AI moderator that facilitates interviews, probes participant answers, and produces transcripts and synthesis outputs. Those are vendor descriptions, not independently validated performance findings. Treat generated themes and summaries as working material to check against the underlying sessions. Decode explains its approach to AI-moderated interviews.
Plan a first study that can answer one useful question
1. Choose a decision-relevant research question
Start with a bounded question about a particular behavior, experience, or decision—not a broad aim such as “understand our users.” For example, ask what makes a specific step in a product setup difficult for people who have recently completed it. A focused question helps you decide what to ask, whom to recruit, and what evidence would be useful.
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2. Define who should take part
Specify the experience or behavior that makes someone relevant to the question. Screen for that experience rather than relying only on demographic categories. When recruiting, make sure the criteria map to the study question; otherwise, interviews may be articulate but not informative for the decision at hand.
3. Decide whether AI is the right moderator
Choose a moderation approach based on the sensitivity of the topic, how much rapport or improvisation the conversation needs, the desired scale and turnaround, and the signals you need to observe.
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| Approach | Best fit | Trade-off |
|---|---|---|
| AI moderation | Structured questions where consistency and parallel sessions are useful. | Probing depends on the guide and configuration; it may not follow an unexpected thread as flexibly as a person. |
| Human moderation | Sensitive, high-stakes, or deeply exploratory conversations that call for rapport and improvisation. | Does not offer the same opportunity to run sessions in parallel. |
| Hybrid | Use AI interviews to surface patterns, then human sessions to investigate selected issues more deeply. | Requires planning and interpretation across two kinds of sessions. |
Great Question’s guide similarly describes AI-led interviews with adaptive follow-ups and session outputs, while noting the value of human moderators for sensitive, high-stakes, and exploratory research. Its platform details may change; see Great Question’s guide to AI-moderated interviews.
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4. Write questions and probing rules
Give the moderator a brief introduction, context and consent language, core questions, and instructions for when to seek an example or clarification. Pair each main question with likely follow-up prompts so the intended depth is clear. Keep questions tied to the research question and avoid asking several things at once.
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For instance, a core question about where someone hesitated during setup can be paired with a prompt to ask for a specific moment or what they expected to happen. The rule should make the desired probe explicit; open-ended probing is not a substitute for deciding what evidence the study needs.
5. Set the participant-facing session details
Configure the modality, language, expected session length, probing depth, and disclosures to fit the study and participant group. Review the full participant-facing flow, including consent language and the transition into the interview, before sending invitations. The exact controls available can depend on the current Mira interface; use the settings presented in your account rather than assuming a particular label or path.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Test, launch, and monitor the study
6. Run a test session
Complete the study as a participant would. Check whether the questions are clear, follow-ups feel appropriate, the session flows naturally, and the technology creates friction. Revise the guide or configuration when the test exposes confusion, repetition, or an unhelpful probe.
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7. Launch and watch for friction
As sessions come in, look at completion patterns and session behavior for signs that participants are getting stuck or interpreting a question differently than intended. Apply the platform’s available quality controls and review a sample of transcripts. If a problem appears in the question or flow, address it before treating later responses as comparable to earlier ones.
Interpret transcripts and themes with human judgment
Use Mira’s transcripts, themes, and summaries as starting points for analysis, not as final findings. Check representative claims in the underlying sessions, including whether a theme accurately reflects what participants meant and whether important differences have been flattened. Base product or research decisions on the participant evidence and the question you set—not on a generated summary alone.
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