Sampling setup
Target population, segment definitions, demographic assumptions, behavioral assumptions, quota ideas, screener logic, and sample logic.
Synthetic respondent simulation
Silicon Sampling
Use silicon sampling for LLM survey research to simulate survey-style responses, synthetic respondents, audience segments, assumptions, and validation questions before fieldwork.
Built for scenario planning, not certainty. Every output should be reviewed against real evidence before an operating decision.
Scenario tape / illustrative
Silicon sampling for synthetic survey rehearsal
R1
Target populationSegment logic mapped
R2
Synthetic respondentsSurvey-style reactions compared
R3
Research teamValidation gaps ranked
Sample type
Synthetic respondents
AI risk
False representativeness
Next proof
Real respondent check
Input
Audience, questions, evidence, segments
Engine
LLM respondent and assumption simulation
Output
Synthetic patterns and validation gaps
What to bring
Bring the sample logic before simulating responses.
Silicon sampling works best when the source includes a research question, target population, audience segments, demographic or behavioral assumptions, survey prompts, concept claims, prior evidence, and the decision the synthetic sample should inform. This page covers AI-generated respondents for research design, not semiconductor engineering samples.
Survey or research prompt
Survey questions, concept descriptions, messages, claims, answer formats, stimuli, product context, and the decision the research should support.
Evidence and context
Prior surveys, interview notes, reviews, support tickets, sales notes, category research, known objections, competitor context, and customer language.
Where silicon sampling helps
Use synthetic respondents as hypotheses, not market truth.
Silicon sampling can help teams rehearse survey logic, compare simulated respondent groups, and find assumptions before fieldwork, as long as the output is treated as research design input rather than representative data.
Survey rehearsal01
See how synthetic respondent groups may answer.
Compare simulated responses across audience segments so unclear questions, weak answer choices, and missing follow-ups appear before real fieldwork.
Assumption check02
Expose what drives the simulated response.
Inspect whether segment assumptions, prompt wording, prior evidence, or category context is shaping the silicon sample too strongly.
Validation plan03
Decide what needs real respondents next.
Turn synthetic patterns into real survey checks, panel questions, interviews, analytics reviews, or smaller pilots your team can run.
Silicon sampling needs source evidence, sample logic, and validation discipline.
MiroFish maps target audiences, segment assumptions, survey prompts, evidence, and decision context into LLM respondent simulations while keeping synthetic response patterns and validation gaps inspectable.
Stage 1
Ground the scenario
Upload source material and define the decision, event, or message you want to test.
Stage 2
Build the actor graph
Map the people, groups, incentives, constraints, and memory that shape the reaction.
Stage 3
Run reaction rounds
Let the simulated actors respond over multiple rounds so the second-order path appears.
Stage 4
Question the report
Review the trajectory, risks, weak assumptions, and what evidence would change the conclusion.
What the report should answer
The report should clarify what the silicon sample can and cannot prove.
- Which synthetic respondent groups react differently
- Which assumptions drive the simulated responses
- Which question wording or segment logic may distort results
- Which patterns look plausible but need real validation
- Which real survey, panel, interview, pilot, or analytics check should run next
Boundary conditions
Synthetic respondents are not real respondents.
- Not representative measurement, completed fieldwork, or real human respondent data
- Not semiconductor sampling, chip validation, or hardware engineering sample management
- Not a substitute for survey panels, interviews, analytics, experiments, or purchase behavior
- Not guaranteed prediction of real human behavior, demand, conversion, or purchase intent
- Not useful when the audience, sample logic, source evidence, or decision context is undefined
Why MiroFish
Silicon sampling should make assumptions visible.
Generic AI can invent respondent answers. MiroFish treats synthetic respondents as a rehearsal layer, showing segment assumptions, simulated response paths, and the real validation step that should follow.
Sample role
Generic AI
Synthetic answers treated as data
MiroFish
Synthetic respondent hypotheses with visible assumptions
Research quality
Generic AI
One averaged survey response
MiroFish
Segment-level response patterns, prompt risks, and source gaps
Next action
Generic AI
Confident synthetic insight
MiroFish
Real survey, panel, interview, pilot, or analytics validation
FAQ
Silicon sampling, without pretending synthetic respondents are real people.
Use it when your team wants to rehearse survey-style responses, compare synthetic respondent groups, and design better validation before fieldwork.
What is silicon sampling?+
Silicon sampling is the use of AI-generated or LLM-generated synthetic respondents to simulate survey-style responses and audience reactions before running research with real people.
How does MiroFish use silicon sampling?+
MiroFish turns your audience assumptions, survey prompts, prior evidence, and decision context into synthetic respondent groups, simulated response patterns, source gaps, and validation questions.
Is silicon sampling the same as real survey sampling?+
No. Real survey sampling recruits human respondents and can support measurement when designed correctly. Silicon sampling is a rehearsal layer for research design, not representative respondent data.
What should I upload?+
Upload a research question, target audience, segment assumptions, survey prompts, concept descriptions, prior survey results, interview notes, reviews, support notes, category research, or known objections.
Can silicon sampling replace real respondents?+
No. It can help find assumptions, question issues, and validation priorities, but important decisions still need real respondents, panels, interviews, analytics, experiments, or behavioral data.
Related simulation paths
Follow the risk into the next decision.
Most decisions do not stay inside one category. These adjacent use cases help teams test the next market, customer, or public reaction path.
Survey simulation
Consumer Surveys
Simulate consumer survey-style responses, segment themes, objections, and research gaps before fielding a panel.
Open Consumer SurveysSurvey research
Survey Cost Reduction
Rehearse survey design, synthetic responses, segment logic, and validation questions before buying sample.
Open Survey Cost ReductionGenerative research
Generative AI Market Research
Synthesize customer evidence, simulate audience reactions, draft research questions, and find validation gaps.
Open Generative AI Market ResearchSynthetic, not final
Use silicon sampling to rehearse the research before fieldwork.
Bring the audience, survey prompts, assumptions, and source evidence. MiroFish will turn them into silicon sampling output with synthetic response patterns and validation gaps.
Start silicon sampling