IV Idea Validation Workflow
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Founder field manual · Low-contact validation

Test the idea before you build the product.

A structured, asynchronous workflow for finding real pain, making a concrete offer and letting customer commitment—not enthusiasm—decide what deserves to be built.

The model
Signal Proposition Commitment Learning Decision

How to use it

One idea. One short test cycle.

Work through each stage in order. Mark an exit gate only when the evidence supports it. Run no more than one or two ideas at once.

The job is not to prove the idea is good.
It is to decide, cheaply and quickly, whether the next level of effort is justified.
00

Frame the idea

Name the real job.

Do not start with a feature list. Start with a specific person, a repeated job, the measurable result they need and the frustration they already tolerate.

Framing statement

For [specific person] who needs to [job they are already trying to do], this helps them achieve [measurable outcome] without [current frustration or cost].

01

Research existing pain

Find behaviour, not agreement.

Use AI to accelerate discovery, never as the judge. Look for traceable evidence that people are already struggling, improvising or spending.

Evidence sources and search patterns

SourceWhat it revealsUseful search pattern
Product reviewsIncumbent gaps and switching triggers"[competitor]" review "wish" · "too expensive"
Reddit & specialist forumsUnfiltered language and workaroundssite:reddit.com "[job]" "how do you"
LinkedIn postsProfessional pain and role languagesite:linkedin.com/posts "[problem phrase]"
Job advertsProof that companies pay people to solve it"[job]" "responsibilities"
Directories & app storesDemand, categories and low-star complaintsSearch the category; read low-star reviews
Search suggestions & PAAHow potential users phrase the jobType the job, problem and alternatives
Help centres & communitiesRepeated support friction"[competitor]" community · help

Evidence log

0 / 10 useful items

AI research prompt

Act as a market-research analyst. For the audience [persona] and job [job], identify repeated complaints, current workarounds, paid alternatives and the exact language people use. Separate direct evidence from inference. Return a table with source type, insight, problem phrase, willingness-to-pay clue, and community or channel to investigate. Do not invent quotes or sources.
02

Locate communities

Go where help is already exchanged.

You do not need a large audience. Find concentrated places where your specific people already share recommendations, workarounds and complaints.

Name the identitySearch the role, not merely the problem.
Follow the toolsAdd community, forum, Slack, Discord, group, webinar or newsletter to each incumbent.
Follow the eventsAssociations, speakers and sponsors often reveal established communities.
Follow the languageSearch exact complaint phrases from reviews and posts in quotation marks.
Follow the creatorsRead the comments and resource pages of trusted writers, hosts and educators.

Community scorecard

Score each criterion from 0–2. Prioritise communities at 7/10 or above, then read the rules before participating.

Community / channelDensityRelevancePermissionActivityAccessTotal
0/10
0/10
0/10

No community has reached the 7/10 priority threshold yet.

Pilot post template
I am exploring a small tool for [specific job]. It is aimed at people who currently [manual workaround] and want [outcome]. I am looking for a few people to try an early pilot and tell me, asynchronously, where it fails. If this is relevant, reply or use [link]. I will not add anyone to a mailing list without permission.
Three-question form
  1. When did you last deal with this problem?
  2. What did you do instead, and what did that cost in time, money or risk?
  3. If a tool solved it reliably, what would need to be true for you to try or pay for it?
03

Make a testable offer

One audience. One outcome. One action.

Create a single page, not a full product. Make the offer concrete enough that the right person can accept or reject it without decoding vague product language.

Offer preview

[Audience]: achieve [outcome] without [current friction]. From [price]. [Commitment action].

Avoid “revolutionary”, “AI-powered” and “all-in-one” unless the words clarify something concrete. Never ask only, “Would you use this?”

04

Test for commitment

Measure what people give up.

Interest is not equal to intent. Signals become more useful as prospects invest time, data, trust or money.

Pre-commit the decision threshold

Set the gate before promotion to prevent excitement from changing the rule after the results arrive.

Threshold not yet met. Keep testing the stated offer with the stated audience.
05

Run a concierge pilot

Deliver before you automate.

Provide the promised result manually or with lightweight tools. The pilot reveals the real workflow, exceptions and value drivers before software hardens your assumptions.

Written feedback

06

Decide deliberately

Make the evidence decide.

End every short test cycle with an explicit outcome. Preserve rejected ideas and the evidence behind them so you do not repeat the same assumptions later.

Reusable summary

One-page worksheet

A compact record for comparing ideas or handing the evidence to your future self.

Weekly cadence

MondayFrame one idea and complete the evidence sweep.
TuesdayShortlist and score communities.
WednesdayPublish the smoke test or pilot invitation.
ThursdayCollect form responses and clarify asynchronously.
FridayReview commitment evidence and make one explicit decision.
ResearchSearch engines, Reddit, public reviews, app directories and AI-assisted synthesis.
CaptureA spreadsheet or Notion-style database.
FormsGoogle Forms or Tally's free tier.
Landing pageA static page, GitHub Pages, Carrd or a shared document.
SchedulingA booking link with “asynchronous questionnaire preferred”.
Testing panelsStudy User Interviews, Respondent, Wynter, PickFu and Pollfish; replicate the method publicly first.

Guardrails

  • Do not represent a prototype as a finished product.
  • Follow community rules and never scrape private spaces.
  • Treat AI-generated claims as hypotheses until evidence supports them.
  • Do not over-value compliments from people outside the target audience.
  • Keep an evidence log so decisions remain reversible and explainable.
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