Conversation Question Designer

Conversation Question Designer

When to Use

You are preparing for a customer conversation and need questions that will produce honest, useful data rather than false validation. Typical situations:

Before starting, verify:

Mode: Hybrid — The agent designs the question script and analyzes/rewrites questions. The human conducts the actual conversation.

Context & Input Gathering

Required Context (must have — ask if missing)

Observable Context (gather from environment)

Default Assumptions

Sufficiency Threshold

SUFFICIENT when ALL of these are true:
Product idea or problem area is known
At least 1 learning goal is identified
Customer type is known or defaulted
PROCEED WITH DEFAULTS when:
Product idea is known but learning goals are vague
Customer type is approximate ("probably small businesses")
MUST ASK when:
No product idea or problem area at all
User provides questions but no context on what they are building

Process

Step 1: Extract Learning Goals

ACTION: Identify the 3 most important things the user needs to learn from upcoming conversations. If the user has provided learning goals, validate them. If not, derive them from their product idea and assumptions.

WHY: Questions without clear learning goals produce scattered, unusable data. Every question in the script must connect to a specific learning goal, otherwise the conversation wanders and the user leaves with opinions instead of facts.

IF the user has stated learning goals → validate they are about customer behavior/problems (not about validating the solution) IF learning goals are solution-focused (e.g., "Would people use my app?") → reframe toward the underlying behavior: "How are people currently solving this problem?"

OUTPUT: A numbered list of 3 learning goals, each framed as a behavior or fact to discover (not an opinion to collect).

Step 2: Audit Existing Questions (if provided)

ACTION: Evaluate each question against the 3 customer conversation quality rules (The Mom Test):

| Rule | Test | Failure Pattern | |------|------|----------------| | Rule 1: Their life, not your idea | Does this question ask about the customer's actual behavior, problems, or workflow — or does it ask them to evaluate your concept? | Questions containing "my idea," "this product," "would you use," "do you think" | | Rule 2: Specifics in the past, not generics about the future | Does this question ask about concrete events that already happened — or does it ask for predictions, hypotheticals, or generalizations? | Questions containing "would you," "will you," "do you ever," "how much would you pay," future tense | | Rule 3: Listen more than talk | Does this question invite an open-ended response — or does it lead toward a specific answer? | Yes/no questions, questions with the answer embedded, multi-part questions that overwhelm |

WHY: Bad questions are the default. Most people naturally ask opinion-seeking, future-hypothetical, or idea-validating questions because those feel productive. But the answers to those questions are worthless — people are overly optimistic about the future and will lie to avoid hurting your feelings. Auditing against these 3 rules catches the specific failure modes before the conversation happens.

FOR EACH question in the user's draft list: 1. Rate as PASS, FAIL, or FIXABLE against each of the 3 rules 2. If FAIL or FIXABLE → identify which rule(s) it violates and why 3. Write a rewritten version that passes all 3 rules 4. Note what learning goal this question serves (or flag it as goalless)

IF no existing questions are provided → skip to Step 3.

Step 3: Generate the Question Script

ACTION: Build a structured conversation script organized by learning goal. For each learning goal, create 3-5 questions that progress from broad to specific.

WHY: Conversations need structure to produce usable data, but rigid scripts kill natural flow. Organizing by learning goal lets the human navigate flexibly — they can follow interesting threads while keeping track of what they still need to learn. Starting broad prevents premature zoom (asking about a specific problem before confirming the person even cares about that area).

Question design rules — apply to every question:

1. Ask about their life, not your idea. Frame questions around their current behavior, workflows, problems, and goals. Never mention your product concept unless deliberately testing a later-stage hypothesis.

2. Ask about specifics in the past, not generics about the future. Replace "Would you..." with "When was the last time..." Replace "Do you usually..." with "Talk me through what happened last time..." Past behavior is the only reliable predictor.

3. Use open questions that invite stories. "Talk me through..." and "Tell me about the last time..." produce richer data than "Do you..." or "How often do you..."

4. Include motivation-revealing questions. "Why do you bother?" and "What are the implications?" separate must-solve problems from nice-to-haves.

5. Include commitment-testing questions. "What have you already tried to solve this?" and "How are you dealing with it now?" reveal whether the problem is painful enough to drive action.

6. End with network-expanding questions. "Who else should I talk to?" and "Is there anything else I should have asked?" generate warm introductions and catch blind spots.

Script structure per learning goal:

## Learning Goal: [goal description]
Opener (broad, non-leading)
[Question that explores whether this area matters to them at all]
Depth questions (specific, past-focused)
[Question about concrete past behavior]
[Question about current workflow/workaround]
[Question about implications/severity]
Commitment signal (reveals real vs stated priority)
[Question about what they have tried or are spending]

Step 4: Add Anti-Bias Safeguards

ACTION: Review the complete script for common bias traps and add inline warnings where the human might accidentally slip into bad patterns during the conversation.

WHY: Even with good prepared questions, conversations go off-script. The human needs to recognize danger signals in real-time. Adding warnings at specific points in the script acts as a field guide during the conversation.

Bias traps to flag:

| Trap | Signal | Recovery | |------|--------|----------| | Compliment fishing | You just described your idea and they said "That sounds great!" | Deflect: "Thanks — but tell me, how are you currently handling this?" | | Accepting vague enthusiasm | They say "I would definitely use that" or "That is exactly what I need" | Anchor: "When was the last time this problem came up? Walk me through what happened." | | Pitching instead of asking | You have been talking for more than 30 seconds without asking a question | Stop and ask: "Sorry, I got excited. Can I ask — how are you dealing with this right now?" | | Leading toward your solution | Your question contains your product's features or approach | Reframe around their problem, not your solution | | Accepting generic claims | They say "I usually..." or "I always..." or "I never..." | Anchor: "Can you tell me about the most recent specific time?" |

Step 5: Produce the Deliverable

ACTION: Write the final question script as a structured document the user can reference during their conversation.

WHY: The deliverable must be usable in the field — not a theoretical analysis. It should fit on a single page, be scannable during a live conversation, and clearly connect each question to what the user is trying to learn.

Output format:

# Conversation Question Script
Context
Product/Problem Area: [from input]
Target Customer: [from input]
Date Prepared: [today]
Learning Goals
1. [Goal 1]
2. [Goal 2]
3. [Goal 3]
Question Script
Learning Goal 1: [description]
Opener: [broad question]
Depth:
[specific past-focused question]
[workflow/workaround question]
[implication/severity question]
Commitment signal: [what-have-you-tried question]
> WATCH OUT: [relevant bias trap warning]
Learning Goal 2: [description]
[same structure]
Learning Goal 3: [description]
[same structure]
Closing Questions (use for every conversation)
"Who else should I talk to?" — generates warm intros
"Is there anything else I should have asked?" — catches blind spots
Quick Reference: Bias Recovery
| If you hear... | Do this... |
|----------------|-----------|
| "That sounds great!" | Deflect → ask about their current process |
| "I would definitely..." | Anchor → ask about most recent specific instance |
| "I usually/always/never..." | Anchor → "Tell me about the last time" |
| Feature requests | Dig → "Why do you want that? What would it let you do?" |
Questions Rewritten (if audit was performed)
| # | Original (FAIL) | Rewritten (PASS) | Rule Violated |
|---|-----------------|------------------|---------------|
| 1 | [original] | [rewritten] | [rule 1/2/3] |

IF the user provided a file path or working directory → write the output to question-script.md ELSE → present the output directly in the conversation

Examples

Scenario: Founder with draft questions about a project management tool

Trigger: "I'm building a project management tool for freelancers. Here are my interview questions: 1) Do you think a simpler project management tool would be useful? 2) Would you pay $10/month for it? 3) How do you manage your projects?"

Process: 1. Extract learning goals: (a) Do freelancers have project management pain? (b) What do they currently use? (c) Would they pay for a solution? 2. Audit existing questions: - Q1 FAIL (Rule 1: asks them to evaluate your idea; Rule 2: hypothetical) - Q2 FAIL (Rule 1: about your product; Rule 2: hypothetical pricing) - Q3 FIXABLE (Rule 2: too generic, needs past-tense anchoring) 3. Generate rewritten script with depth questions

Output (abbreviated):

## Questions Rewritten
| # | Original (FAIL) | Rewritten (PASS) | Rule Violated |
|---|-----------------|------------------|---------------|
| 1 | "Do you think a simpler PM tool would be useful?" | "Tell me about the last project you managed. What went well and what was frustrating?" | Rule 1, Rule 2 |
| 2 | "Would you pay $10/month for it?" | "How much time do you spend on project admin each week? What tools are you paying for right now?" | Rule 1, Rule 2 |
| 3 | "How do you manage your projects?" | "Walk me through how you managed your most recent client project from start to finish." | Rule 2 |
Learning Goal 1: Do freelancers have project management pain?
Opener: "What does a typical work week look like for you?"
Depth:
"Tell me about the last time a project got disorganized. What happened?"
"What are the implications when project details fall through the cracks?"
"How seriously do you take project organization compared to, say, finding new clients?"
Commitment signal: "Have you looked into any tools or systems to help with this?"

Scenario: Product manager preparing for first customer discovery conversations

Trigger: "We're exploring a new feature for our analytics platform that would auto-generate executive reports. I need to prepare questions for talking to marketing directors. No existing questions yet."

Process: 1. Identify learning goals: (a) How do marketing directors currently create executive reports? (b) How painful is the current process? (c) Who else is involved in reporting? 2. Skip audit (no existing questions) 3. Generate full question script from scratch 4. Add anti-bias safeguards (flag the risk of pitching the auto-generate feature)

Output (abbreviated):

## Learning Goal 1: Current reporting workflow
Opener: "Tell me about the last executive report you put together."
Depth:
"Walk me through the steps — where did the data come from, who was involved, how long did it take?"
"What was the most annoying part of that process?"
"What happened after you delivered it? Any follow-up requests or revisions?"
Commitment signal: "Have you tried any tools or shortcuts to speed this up?"
> WATCH OUT: Do NOT mention "auto-generated reports" during this section.
> If they mention reporting pain, resist the urge to pitch — instead ask
> "What have you tried to make it faster?"

Scenario: Rewriting a single biased question

Trigger: "Is this a good question to ask potential customers: 'Would you buy an app that helps you track your calories more easily?'"

Process: 1. Identify the single question violates Rule 1 (about your app) and Rule 2 (hypothetical future) 2. Rewrite to pass all 3 rules 3. Provide the rewritten version with explanation

Output:

FAIL — violates Rule 1 (asks about your idea, not their life) and Rule 2 (hypothetical future).
The answer will almost always be "yes" because people are optimistic about
hypotheticals and want to be supportive. This tells you nothing.
Rewritten:
"How are you tracking what you eat right now?" (their current behavior)
"Walk me through what you did for food tracking yesterday." (specific past)
"What else have you tried? What did you like and hate about it?" (commitment signal)
"How much are you spending on health/nutrition apps right now?" (price anchor from reality)
These questions reveal whether they actually care enough to have tried
solving this — which is far more predictive than whether they say "yes"
to a hypothetical.

Key Principles

References

License

This skill is licensed under CC-BY-SA-4.0. Source: BookForge — The Mom Test by Rob Fitzpatrick.

Related BookForge Skills

This skill is standalone. Browse more BookForge skills: bookforge-skills

Scenario: Founder with draft questions about a project management tool

Trigger: "I'm building a project management tool for freelancers. Here are my interview questions: 1) Do you think a simpler project management tool would be useful? 2) Would you pay $10/month for it? 3) How do you manage your projects?"

Process: 1. Extract learning goals: (a) Do freelancers have project management pain? (b) What do they currently use? (c) Would they pay for a solution? 2. Audit existing questions: - Q1 FAIL (Rule 1: asks them to evaluate your idea; Rule 2: hypothetical) - Q2 FAIL (Rule 1: about your product; Rule 2: hypothetical pricing) - Q3 FIXABLE (Rule 2: too generic, needs past-tense anchoring) 3. Generate rewritten script with depth questions

Output (abbreviated): ```