PH Deck

Users face challenges in building and refining prompts for AI models, which may result in difficulties in generating high-quality outputs and hinder the effective deployment of large language models.

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

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

Developers and engineers managing large language models (LLMs) struggle with observability, performance tracing, and effective management of prompts and evaluations. Lack of effective tools for tracing, evaluating, and managing prompts complicates the development and optimization of LLM applications.

Users face challenges in signing PDF documents online, which can be time-consuming and insecure.

The open source community faces challenges in connecting developers, maintainers, and groups with potential sponsors, which inhibits the growth and sustainability of projects due to limited visibility and access to sponsorship opportunities.

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.

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

Users in India rely on proprietary trading platforms with limited customization, delayed data, and no integrated algo trading support.

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.