PH Deck

Teams currently manage LLM prompts in ad-hoc formats (text files, spreadsheets) leading to collaboration inefficiencies, inconsistency, and lack of testability

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

Users exploring the capabilities of Large Language Models (LLMs) often face difficulty in coming up with effective prompts to fully leverage these models' potential. This leads to suboptimal usage and experimentation, hindering learning and application in various contexts. The drawbacks include a lack of inspiration, inefficiencies in understanding the full capabilities of LLMs, and an inability to apply these models effectively across different domains.

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.

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.

Currently, users struggle with creating comprehensive, multi-file AI prompts, which hampers the efficiency in tasks such as writing, coding, and research.

Creating comprehensive, multi-file AI prompts is a complex task leading to inefficiencies.

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 manually trim tokens, enforce budgets, and select models for LLM prompts, leading to higher costs and time-consuming optimization.

Users manually manage AI prompts across different platforms, leading to inefficient workflow and disorganization when accessing or updating prompts.

Users need an effective way to detect hallucinations, evaluate prompts, select the right data for fine-tuning models, and track performance version after version,which is challenging with the existing tools and methods.