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Train AI to generate questions and collect data

Hacker News

Train AI to generate questions and collect data

Hey everyone, I was doing research for a different product using Google Forms and was struggling with form completion. Maybe that product wasn’t a great idea, but I did some digging and learned that forms in general have a high abandonment rate (+ 60%). I thought, what if I fed GPT a bunch of data points and let AI decide how to collect it. We prototyped a chatbot that dynamically generated questions based on how users interacted with it, shared a direct link and people were way more engaged, seeing completion rates over 80% We ended up building out a GPT chat product that replaces forms. You just tell AI what data to collect and it autonomously decides what questions to ask, when to ask them, and how to ask them to collect data, and delivers the data to Google Sheets. You can train it using your own business data so it will generate responses, but its primary function is to generate questions, collect and deliver data. Obvious use-case is lead generation, but it's useful for general research and feedback collection, even recruiting. Shameless link share, but I dropped it on Product Hunt and we would love support (It was engineered by a teenager so showing support there can be motivating for a teen interested in CS and AI) https://www.producthunt.com/posts/botsheets-chat-2 Here is the product: https://www.botsheets.com Thanks so much!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
88%88% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: google, user, using · Missing: mac, agents, macos
83%83% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: google, users, way · Missing: mobile apps, ios, personal
49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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