PH Deck

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 who frequently use AI models struggle to efficiently access and manage their AI prompts.

Switching tabs and constantly copy-pasting are common practices that are inefficient and time-consuming.

No centralized way to organize and quickly access prompts across different AI platforms is a major drawback.

Users struggle to test language model (LLM) prompts and configurations efficiently, facing slow testing processes and difficulty comparing results side-by-side.

Prompt engineers often struggle with streamlining their prompt workflow and simplifying version control, which can lead to inefficiency and difficulty in managing multiple versions of their projects.

Users currently rely on manual methods to manage AI prompts, which are often disorganized and lack efficiency.

Drawbacks include difficulty to 'organize, refine, and optimize AI prompts efficiently' resulting in a loss of productivity and effectiveness.

ChatGPT is a powerful tool, but 99% of people don't know how to use it efficiently. Users struggle to leverage its full potential, lacking knowledge on effective prompt crafting for various use cases.

Users face challenges in iterating on and improving their Large Language Model (LLM) applications, such as LLM tracing, metrics, evaluations, and prompt management.

Current methods lack efficient llm-as-a-judge functionality, as well as comprehensive datasets testing options.

Users struggle to efficiently store, organize, and manage their AI prompts, leading to cumbersome and disjointed AI platform interactions.

The drawback of the old situation is .

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.

The current situation for users is managing projects without tailored guidance or prompts. Users often rely on generic templates or their intuition to handle various project management tasks, which can be ineffective and uninspiring.

The drawbacks of this old situation include difficulties in coming up with relevant prompts for diverse project scenarios, which can lead to inefficient project workflows.