PH Deck

Users manually set up RAG systems, requiring technical expertise and time-consuming integration of components. Key drawbacks: manual setup of RAG systems, complex integration of components, and lack of intuitive interfaces.

Flutter developers and beginners face difficulties with traditional UI development, which involves writing complex code, taking a lot of time and possibly leading to inefficiencies.

Users building AI-powered apps with RAG face complex infrastructure setup, scalability challenges, and ongoing maintenance when developing retrieval-augmented generation systems from scratch.

Users seeking to understand and apply AI Retrieval Augmented Generation (RAG) face a lack of centralized resources and difficulty in keeping up with the latest developments and technical knowledge in the field, leading to fragmented learning experiences and potential gaps in understanding.

Users need to connect AI applications to various external data sources quickly and efficiently.

Users manually calculating F1 race results and driver standings

Building RAG applications using user data from multiple sources like Google Drive, Salesforce, and Notion is often complex and time-consuming.

Handling authentication and data syncing across different platforms.

Users need to manually set up and manage complex RAG (Retrieval-Augmented Generation) pipelines for AI applications, which requires significant technical expertise and time. Manual deployment, lack of automation, and scalability challenges hinder efficient implementation.

Users face time-consuming manual creation of wireframes and require advanced design skills to visualize layouts, leading to inefficiency in early-stage product design.

Users struggle with managing short tasks efficiently using traditional timer apps or manual methods, which require multiple steps to set up and lack flexibility. Traditional timer apps or manual methods require multiple steps to set up and lack flexibility