Reprompt – Collaborative Prompt Testing

Hacker News

We recently developed an AI app, but we struggled to write good prompts for it. Although GPT3.5 is affordable, we found it challenging to provide effective instructions. We often made changes to our prompts that unintentionally led to poor performance. Testing prompts is the solution, but existing tools make this task difficult. You must generate an output, review it, delete it, and generate again, which is time-consuming. After only a few rounds of testing, you may think that you have a bulletproof prompt without realizing its flaws. To address this issue, we developed Reprompt, which enables you to generate multiple outputs simultaneously. This feature allows you to test more data and find inconsistencies more easily. Instead of just testing a few times, you can test dozens of times. Moreover, Reprompt automatically analyzes all the outputs, eliminating the need to review each response individually. You only need to specify the expected output, and Reprompt will analyze all the responses and provide a score. This score enables you to determine whether your prompt is improving or not. Lastly, Reprompt lets you share prompts with your team in an easy way so that you can all work together to improve them. Give it a try and let us know your thoughts. Thank you!

Visit product View on Hacker NewsApr 11, 2023

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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

81%81% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

71%71% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, ide · Missing: https docs, excited, just released

35%35% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

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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