Audience Persona Builder: How to Write a Task for the Tool
The Audience Persona Builder tool helps turn a vague idea about the people you are creating something for into clearly labeled hypotheses: who might need your offer, what needs these people have, and what questions should be checked before launch. This is not research and not a ready-made portrait of real customers, but a structured draft for further validation. The quality of the result almost entirely depends on how precisely you fill in two fields: source material and context with requirements. Below we explain what exactly to write there, how to distinguish a weak task from a strong one, and how to evaluate the resulting text.
Open tool: Audience persona builder ↗What the tool does and does not do
The tool takes two text fields as input: source material (up to 8000 characters) and context with requirements (up to 1000 characters). As output, you get one text result in a separate panel, which can be copied or downloaded as a plain TXT file. There are no style presets, sliders, or detail levels in the interface, so all specifics must come from your text.
It is important to understand the boundaries: the tool does not conduct surveys, does not collect statistics, and does not know your real customers. It formulates assumptions based on what you wrote. Therefore, in the task it is useful to explicitly ask to label each block as a hypothesis and indicate what exactly needs to be checked.
The result should be perceived as a framework for interviews, a landing page, or a research brief. If the text contains statements about market size, income, or behavior without a 'hypothesis' label, this is a reason to rewrite the task and explicitly require labeling.
Bad task: 'Describe the target audience of the app.' Good: 'Make a list of hypotheses about users of a meal planning app. Label each item as a hypothesis, indicate the need and a question for validation.'
Source material field: what to put there
In the source material, it makes sense to include only what is relevant: a description of the product or service, for whom it is intended according to your current version, what tasks it solves, how it differs from alternatives, what limitations are already known. The more specific the wording, the less the tool will invent for you.
It is useful to add existing observations: quotes from inquiries, frequent questions, reasons for rejections, comments from correspondence. Even a few real phrases provide more benefit than general words like 'for active people.' If there are no observations, honestly write about it so that the result does not look like a confirmed fact.
Do not upload the entire archive of correspondence or long internal documents: the limit is 8000 characters, and extra noise blurs the focus. It is better to select 5–10 of the most indicative fragments and clearly label where they came from.
Fragment of source material: 'Meal planning app. Users complain that they don't have time to cook according to the plan. Frequent question: can I replace a dish without rebuilding the whole week.'
Context field: goal, format, constraints
Context is a short frame that sets why you need the result and in what form. Specify the goal: prepare questions for interviews, gather a draft of segments, check wording for a landing page. The goal determines what the tool puts first.
Set the output format: for example, a list of hypotheses with subpoints 'need' and 'question for validation,' or a table in text form, or short paragraphs for each segment. Since the interface does not offer templates, the format must be described in words in the context field.
Add constraints: response language, volume, what to avoid. For example, 'do not use demographic stereotypes,' 'do not suggest prices,' 'do not claim that a hypothesis is confirmed.' This reduces the risk of getting general phrases that cannot be validated.
Context: 'Goal — questions for 5 interviews. Format: hypothesis, need, question. Language Russian. Do not invent statistics and do not suggest prices.'
How to formulate hypotheses and questions for validation
A good hypothesis describes a specific situation and an assumed need, not a label. Instead of 'busy parents,' better 'parents who cook dinner after work and want to fit within 30 minutes.' Such a formulation immediately suggests whom to ask and about what.
For each hypothesis, it is useful to ask for a question that can be asked to a real person. The question should allow a detailed answer and not hint at the desired conclusion. Compare: 'Isn't it hard for you to plan a menu?' and 'Tell me how you decide what to cook for dinner on weekdays.'
Ask to label the level of confidence: what is based on your observations and what is invented by the tool. This helps separate working guesses from fantasies and plan validation first for the most risky assumptions.
Hypothesis: parents with two children give up planning because the plan breaks when the schedule changes. Question: 'What happened the last time the weekly plan had to be changed?'
Example: meal planning app
Suppose there is a fictional product: an app that helps busy parents create a weekly menu and shopping list. This is a learning example, not a description of a real service. In the source material, you can put a description of features, three quotes from inquiries, and a known limitation: users do not like rebuilding the entire plan.
In the context, specify the goal — prepare hypotheses for five interviews, format — three blocks 'hypothesis, need, question,' and a ban on statistics and prices. Then the result will be suitable for talking with people, not for a report to investors.
After generation, check that each block can be validated in a conversation in 15–20 minutes, that the questions are open-ended, and that nowhere is it stated that the hypothesis is already confirmed. If a block is too general, go back to the context field and clarify the situation.
Expected fragment of the result: 'Hypothesis (not validated): parents are willing to change one dish but not the whole plan. Need: flexibility without losing the shopping list. Question: what do you do when one dish doesn't fit?'
How to check the result and what limitations it has
Checking begins with reading aloud: if a hypothesis cannot be explained to a colleague in a minute, it is too vague. Then note which points rely on your data and which appeared only from the task text. The former can be checked later, the latter — first.
Compare the result with real conversations: ask the prepared questions to several people and see which hypotheses fall apart. The tool does not replace interviews, surveys, and analytics; it only prepares material for them. The model may make mistakes, and service availability and request queues may vary.
Consider the technical constraints: text fields are limited to 8000 and 1000 characters, the result is generated once and is not saved automatically, export is only in TXT. Do not expect a ready strategy or guarantees from the tool: it is a draft of hypotheses that needs to be validated with real people.
Checklist: hypothesis is specific, question is open-ended, no invented statistics, there is a note about being unvalidated, it is clear whom to ask.
A precise task for the Audience Persona Builder is built from two things: specific source material with real observations and a short context with goal, format, and constraints. Ask to label hypotheses, add questions for validation, and do not present guesses as facts. The resulting text is not a portrait of a real audience, but a working list of assumptions that needs to be checked in conversations with people. This approach saves time and reduces the risk of building a product on convenient but unconfirmed ideas.
Open tool: Audience persona builder ↗Interface translations are machine-assisted and may need refinement.