--- title: "How Minds Builds Synthetic Research Panels | Minds" canonical_url: "https://getminds.ai/research/methodology" last_updated: "2026-08-13T13:08:08.696Z" meta: description: "How Minds creates reusable AI-persona groups, collects parallel synthetic responses, and fits into evidence-conscious research workflows." "og:description": "How Minds creates reusable AI-persona groups, collects parallel synthetic responses, and fits into evidence-conscious research workflows." "og:title": "How Minds Builds Synthetic Research Panels | Minds" "twitter:description": "How Minds creates reusable AI-persona groups, collects parallel synthetic responses, and fits into evidence-conscious research workflows." "twitter:title": "How Minds Builds Synthetic Research Panels | Minds" --- Minds June 21, 2026·Methodology·Minds Team # **How Minds Builds Synthetic Research Panels** How Minds creates reusable AI-persona groups, collects parallel synthetic responses, and fits into evidence-conscious research workflows. [Run a synthetic research panel](https://getminds.ai/?register=true) Minds is a synthetic market research platform for testing concepts, messaging, positioning, pricing, and audience reactions before teams commit to full fieldwork. This page explains the public methodology behind Minds without exposing proprietary implementation details. ## The Method in Brief Minds follows three visible steps: 1. Define and review a target group from an audience description and permitted source material. 2. Build or reuse the individual Minds in that group. 3. Ask the group research questions and compare the outputs with human evidence where the decision requires validation. The goal is not to invent a fictional average customer. The goal is to create a research-useful simulation of a defined audience segment, with enough context to respond consistently to concepts, claims, messages, and tradeoffs. ## 1. Group Creation Starts With Evidence Minds does not start from a single generic prompt like "act as a customer." Each research group is built around a defined audience, use case, and decision context. Groups can use three product-supported inputs: - **Existing Minds:** reuse personas already available to the workspace. - **Audience descriptions:** describe the roles, segments, contexts, and differences the group should cover. - **Attached files or links:** add permitted research notes, source material, or background documents relevant to the study. The available inputs and creation modes depend on the workspace configuration. The researcher remains responsible for source rights, study design, group review, and deciding what requires human validation. For the public technical overview and boundaries of the source-modelling approach, see [Minds PRISM](https://getminds.ai/research/minds-prism). ## 2. Each Mind Uses Many Small Knowledge Bases Minds are reusable AI personas created from descriptions, profiles, links, files, or research notes. They can be used across chat and group-response workflows. A pricing study, concept test, and positioning study may use the same audience label but require different source material and different validation criteria. This structure lets a panel behave less like one averaged persona and more like a group of individual respondents with overlapping but distinct beliefs, priorities, and objections. ## 3. Research Runs as Interviews, Surveys, and Group Discussions Researchers use Minds for: - Concept testing - Message testing - Positioning research - Pricing and packaging reactions - AI focus groups - Audience and segment exploration - Early qualitative probing before human fieldwork A Mind can answer individually, join a panel readout, compare perspectives with other Minds, or respond to moderator follow-ups. The system is designed to surface disagreement, uncertainty, objections, and the reasons behind a response rather than only producing a summary sentiment score. For the underlying academic category, see our guide to [silicon sampling](https://getminds.ai/blog/silicon-sampling). For the broader commercial workflow, see the [synthetic research guide](https://getminds.ai/blog/synthetic-research). ## Validity Must Be Tested, Not Assumed Synthetic research should be evaluated against relevant human or behavioral evidence. A single accuracy percentage would hide important differences between audiences, questions, stimuli, models, and scoring rules, so this page does not publish a universal Minds accuracy claim. For a defensible validation study, define the task and success metric before running the synthetic group, keep a human comparison set separate, report agreement and disagreement by question, and disclose the audience, inputs, model configuration, sample sizes, and limitations. Do not tune the workflow on the same human responses later presented as independent validation. Minds should not be treated as a universal replacement for human research. Use human fieldwork when: - The decision is high-stakes, regulated, legal, medical, political, or safety-critical. - You need statistical population estimates with confidence intervals. - You need to observe real behavior rather than stated preference. - The audience is poorly represented in available data. - The stimulus depends on physical, sensory, or in-store experience. - Final validation must come from recruited human respondents. The strongest workflow is usually hybrid: use Minds to explore the space, test more variations, refine the instrument, and narrow the options; then use human research for the final questions that need external validation. ## Public Evidence and Governance For an inspectable view of the evidence—not a Minds-authored award—see the [versioned Minds evidence dossier](https://syntheticresearchindex.org/evidence/minds/v0-1) and the [candidate benchmark preregistration](https://syntheticresearchindex.org/benchmark/preregistration/osrb-001-v0-1). Minds founded and funds the Synthetic Research Index. The Index discloses that conflict, records unresolved evidence checks, and publishes a [Minds-specific recusal](https://syntheticresearchindex.org/governance/recusal/minds) that prevents Minds personnel from approving Minds evidence, scores, disputes, or conclusions. The external reviewer seat is currently vacant, so these records should not be described as an independent certification, award, or completed benchmark. ## Key Terms **Synthetic research** uses AI-generated respondents or panels to simulate how a defined audience may react to questions, concepts, messages, and tradeoffs. **Silicon sampling** is the academic method of conditioning large language models on respondent profiles, asking survey-style questions, and comparing the resulting distributions against human survey data. **Synthetic panel** means a structured group of AI respondents built to represent a segment, audience, buying committee, user group, or market category. **AI persona** usually means a single simulated respondent profile. In Minds, a Mind is a reusable AI respondent created from a description, profile, permitted files, links, or research notes; available inputs and behaviors depend on the workspace and plan configuration. **AI focus group** is a moderated session where multiple synthetic respondents react to the same prompt, concept, message, image, or product idea and surface agreement and disagreement. ## What This Makes Possible Traditional research is often rationed because recruitment is slow and budgets are finite. Minds changes the workflow by letting teams ask more questions earlier: - Screen early concepts before paying for full fieldwork. - Compare message options before media spend. - Find objections before sales enablement or launch. - Test positioning before rebuilding a deck or website. - Rehearse qualitative interviews before recruiting humans. - Turn prior research into an interactive panel that teams can query repeatedly. This is why Minds should be used as a decision-support layer, not a replacement for rigor. The workflow is most valuable when it helps teams explore more options and focus human research on the questions that matter. ## Suggested Citation Minds. “How Minds Builds Synthetic Research Panels.” Updated July 31, 2026. https://getminds.ai/research/methodology ## Further Reading - [Silicon Sampling: How LLMs Simulate Survey Responses](https://getminds.ai/blog/silicon-sampling) - [Synthetic Research: The Complete 2026 Guide](https://getminds.ai/blog/synthetic-research) - [AI Focus Groups: How They Work](https://getminds.ai/blog/ai-focus-group) - [The Spark Effect: Creative Diversity in Multi-Agent AI](https://getminds.ai/research/spark-effect-creative-diversity-multi-agent-ai) ## External Context and Ongoing Evidence Review Minds founded and initially funds the [Synthetic Research Index](https://syntheticresearchindex.org), a public evidence registry and category map for this emerging field. That funding relationship is disclosed by the Index, Minds is recused from its own evaluation, and the Index is not presented as an independent award or certification of Minds. For the cross-vendor [classification and evidence protocol](https://syntheticresearchindex.org/methodology), including its limits on what can be compared, see the Index methodology. For new literature and study summaries that remain distinct from product claims, see its [research feed](https://syntheticresearchindex.org/research). ## **Frequently asked questions** ### **What is synthetic research?** Synthetic research uses AI-generated respondents or panels to simulate how defined audience segments may react to questions, concepts, messages, and tradeoffs. It is best used for fast exploration, pre-testing, and decision support. ### **How are Minds groups created?** Minds groups can be created from existing Minds, an audience description, and attached files or links. Researchers should document which inputs were used and review the group before relying on its responses. ### **How is a Mind different from a simple AI persona?** A simple AI persona is often a static profile. A Mind is a reusable AI respondent created from a description, profile, permitted files, links, or research notes. Available inputs and behaviors depend on the workspace and plan configuration. ### **How accurate is Minds?** Minds does not claim one universal accuracy percentage for every audience and question. Teams should validate the workflow for their specific use case against held-out human evidence and treat synthetic responses as directional decision support rather than population estimates. ### **Does synthetic research replace human respondents?** No. Synthetic research is strongest for early concept screening, message testing, objection mining, and research design. Human research remains important for high-stakes, regulated, behavioral, sensory, or final validation work.