程式與產品

Use AI to turn a vague idea into a validatable product spec

Indie developers most often die by "building something nobody wants." This workflow uses AI to quickly do market research, analyze interview transcripts, and write a one-page PRD, confirming direction before you write code.

  1. 1. Use AI for market and competitor research

    Describe your idea to Perplexity and ask it to list existing competitors, pricing models, and user complaints with source links, to quickly judge whether the market has a gap.

  2. 2. Define target audience and pain-point hypotheses

    Use Claude to organize the research into 3 target-audience profiles, each with a hypothesis list of "how they solve it now, what hurts, whether they'd pay," as the basis for later validation.

  3. 3. Generate an interview script and organize transcripts

    Ask Claude to write 8 to 10 open-ended interview questions from the hypotheses, and after actually interviewing 5 potential users, paste the transcripts back and have it extract recurring pain points and verbatim quotes.

  4. 4. Write a one-page PRD

    Have Claude produce a concise PRD from the validated pain points: problem statement, core features (cut down to only what the MVP needs), success metrics, and an explicit out-of-scope list, to avoid feature bloat.

  5. 5. Turn it into a deck to align consensus

    Drop the PRD into Gamma to auto-generate a deck to explain the direction to partners, early users, or potential investors, gather feedback, and revise one more round.

FAQ

Isn't doing this research too early before writing a single line of code?

Just the opposite. Indie developers have limited resources, and coding is the most expensive way to validate. Two or three days of AI research and interviews is far cheaper than two months building a product nobody wants — this step turns "want to build" into "someone wants it."

Is AI-generated market research trustworthy?

Perplexity attaches source links — always click through to verify numbers and the current state of competitors, don't copy blindly. AI is good for quickly laying out the big picture and finding leads; final pain-point validation still relies on real interviews, and the two complement each other.