PH Deck

Users manage AI prompts in generic note apps or documents, leading to disorganization and inefficiency in retrieving effective prompts.

Users struggle with crafting effective AI prompts due to lack of expertise, leading to suboptimal outputs and inefficiency in leveraging AI tools.

Users manually manage AI prompts using disjointed tools like notes or spreadsheets, leading to disorganization, inefficient sharing, and inability to monetize prompts

Users currently store AI prompts in scattered locations like notes apps or spreadsheets, struggling with organizing and retrieving prompts efficiently which leads to duplicated work and wasted time

Developers currently manage AI prompts in closed systems lacking version control, leading to vendor lock-in, privacy risks, and limited control.

Users struggle to craft effective, optimized prompts for AI models like ChatGPT, leading to time-consuming iterations and suboptimal results.

Traditional prompt engineering is complex and inaccessible for those without significant AI experience, making it challenging for users to effectively interact with large language models (LLMs) to obtain useful responses. The complexity and inaccessibility of traditional prompt engineering are the main drawbacks.

Users manually organize and reuse prompts for multiple AI models, leading to inefficiency, disorganization, and lack of advanced features like variables or shortcuts.

Product Managers and Makers often experience difficulty in getting started on various tasks due to the complexity and breadth of their roles. They struggle with prompt engineering, which is crucial for effectively using GPT-based tools, leading to inefficient task execution and productivity loss.

Users struggle with manually creating detailed prompts for each AI application, which is time-consuming and inconsistent, leading to suboptimal AI-generated results and inefficiency in real-world applications like fashion styling analysis.