Go beyond simple prompts. Gather raw data—comments from your social media, competitor book reviews, and podcast feedback—and feed it all into ChatGPT. Then, ask it to synthesize this data into a detailed avatar guide, identify market gaps, and suggest opportunities for your offer.

Expensive user research often sits unused in documents. By ingesting this static data, you can create interactive AI chatbot personas. This allows product and marketing teams to "talk to" their customers in real-time to test ad copy, features, and messaging, making research continuously actionable.

Instead of walking into a pitch unprepared, Reid Hoffman advises founders to use large language models to pre-emptively critique their business idea. Prompting an AI to act as a skeptical VC helps founders anticipate tough questions and strengthen their narrative before meeting real investors.

The ease of AI development tools tempts founders to build products immediately. A more effective approach is to first use AI for deep market research and GTM strategy validation. This prevents wasting time building a product that nobody wants.

AI models tend to be overly optimistic. To get a balanced market analysis, explicitly instruct AI research tools like Perplexity to act as a "devil's advocate." This helps uncover risks, challenge assumptions, and makes it easier for product managers to say "no" to weak ideas quickly.

AI agents can systematically analyze online communities to identify recurring user pain points and underserved market segments. This data-driven approach uncovers validated business ideas directly from potential customers' candid conversations, as shown by the "backyard chickens" example.

Create a powerful research workflow by extracting text from relevant Reddit threads and feeding it into ChatGPT. Prompt the AI to summarize the most common topics, questions, and pain points. This quickly distills the core language and concerns of a niche community, informing content and product strategy.

To truly validate their idea, Moonshot AI's founders deliberately sought negative feedback. This approach of "trying to get the no's" ensures honest market signals, helping them avoid the trap of false positive validation from contacts who are just being polite.

Instead of immediately building, engage AI in a Socratic dialogue. Set rules like "ask one question at a time" and "probe assumptions." This structured conversation clarifies the problem and user scenarios, essentially replacing initial team brainstorming sessions and creating a better final prompt for prototyping tools.