Use synthetic respondents to generate hypotheses, pressure-test an instrument, and explore plausible objections before fieldwork. Use real respondents to measure actual attitudes, behavior, prevalence, willingness to pay, or segment differences. The strongest workflow uses simulation to improve the human study, not to claim that generated responses are a population sample.
Definitions
What is the difference between synthetic and real respondents?
A synthetic respondent is an AI-generated response conditioned on a prompt, persona, or source packet. A real respondent is a person reporting an attitude, memory, intention, or behavior through a survey, interview, usability session, or focus group.
The difference is not simply digital versus human. It is the source of the evidence. Synthetic output reflects model training, supplied context, prompt design, and generation settings. Human research reflects recruited people, the sampling frame, the research instrument, and the conditions under which they answer.
Both can be poorly designed. A vague persona can produce a polished stereotype, while a biased sample of real people can produce a misleading estimate. The research question determines which failure mode matters.
Decision matrix
How do synthetic and real respondents compare?
Synthetic respondents optimize speed and iteration; real respondents provide direct human evidence. Neither advantage transfers automatically to the other method.
| Dimension | Synthetic respondents | Real respondents |
|---|---|---|
| Best use | Hypothesis generation and rehearsal | Measurement and direct evidence |
| Setup | Source packet, personas, prompts, constraints | Recruitment, screener, sample, instrument |
| Speed | Minutes to hours | Days to weeks |
| Representation | Not representative by default | Can be designed for a target population |
| Lived experience | Generated approximation | Reported by the participant |
| Primary risk | Stereotypes, prompt sensitivity, false confidence | Sampling, response, moderator, and measurement bias |
Good fit
When should product teams use synthetic respondents?
Use synthetic respondents before expensive or irreversible work, when the immediate goal is to find weak assumptions and better questions rather than estimate a population value.
- Rehearse survey questions and identify wording that produces ambiguous answers.
- Generate a broader objection set for positioning, pricing, onboarding, or launch messages.
- Compare plausible reactions across clearly stated audience assumptions.
- Stress-test a research plan before recruiting participants or purchasing panel sample.
- Map what evidence is missing and which claims need real-world validation first.
Human evidence
When are real respondents still required?
Real respondents are required whenever the decision depends on what people actually believe, feel, remember, choose, or pay—not what a model can plausibly generate about them.
- Representative estimates, incidence rates, market sizing, and statistically defensible segment comparisons.
- Purchase intent, price sensitivity, accessibility, safety, health, legal, or high-stakes policy decisions.
- Research involving rare populations, changing subcultures, local context, or underrepresented groups.
- Discovery work where unexpected language and counterintuitive behavior are the value of the study.
Hybrid method
What does a responsible hybrid research workflow look like?
A responsible workflow uses synthetic output to prepare human research, then uses human evidence to confirm, reject, or recalibrate the simulated claims.
- 1
State the decision
Write the product or GTM decision, the affected audience, and the cost of being wrong.
- 2
Ground the simulation
Upload interviews, reviews, win-loss notes, product context, competitor evidence, and known constraints.
- 3
Record hypotheses
Treat objections, themes, and segment differences as propositions to test—not findings.
- 4
Collect human evidence
Use interviews, surveys, usability sessions, sales calls, or live experiments appropriate to the decision.
- 5
Compare and document
Record where simulation and human evidence agree, diverge, or remain unresolved.
Simulation is not a representative survey or a deterministic forecast.
MiroFish output is designed for hypothesis generation, scenario stress testing, and research preparation. Do not present generated actors, dialogue, percentages, or reaction paths as observations from real customers or a statistically representative population.
Wake-up zone
What are the key takeaways?
- Synthetic respondents are generated evidence, not recruited participants.
- Use them for rehearsal, question design, and hypothesis expansion.
- Use real people for measurement, lived experience, and decisions requiring representative evidence.
- Document disagreements between simulated and human results instead of averaging them away.
Frequently asked questions
What should teams know before using this method?
Are synthetic respondents accurate?
Accuracy varies by model, prompt, question, population, and outcome. Agreement on an average does not prove that every item or segment is valid, so important claims need human validation.
Can synthetic respondents replace customer interviews?
No. They can help prepare an interview guide and surface hypotheses, but they cannot report a customer's lived experience or reveal genuinely new behavior in the way a real participant can.
Can synthetic respondents be statistically representative?
Not by default. A set of generated personas is not a probability sample, and its size does not create statistical representativeness.
What should I upload before running a simulation?
Use current interviews, reviews, support tickets, win-loss notes, product details, audience definitions, market evidence, and the exact decision you need to make.
Primary research to review
Research before fieldwork
Turn audience assumptions into questions your team can validate.
Bring a source packet, a defined audience, and a real decision. MiroFish will surface plausible reaction paths and the evidence gaps that still need people.
Start an audience simulationContinue the cluster