About This Site
A collaborative platform for designing, testing, and refining LLM workflows. PromptGruup is a collaborative prompt engineering platform designed to boost the time to deployment for LLM workflows. It empowers developers and prompt engineers with advanced tools to design and quickly iterate their LLM workflows through a node-based interface. It offers features like version control, structured API chaining, and response parsing to ensure AI-driven applications work with efficiency and precision.
Alternatives
Promptblocks
Social prompt engineering platform for AI app developers to manage and share prompts.
Promptblocks is a social prompt engineering platform designed for AI app developers. It allows users to save, reuse, and refine their prompts, share them with the community, and deploy them effortlessly.
Prompt Picker
Prompt Picker helps you choose the best system prompts for your LLM applications.
Prompt Picker helps you choose better system prompts (aka custom instructions) for your LLM-powered applications by running experiments over simulated user inputs. In other words, it helps answer "...but which *exact* system prompt should I be using?"
Key Features AI
Core Features
Node-based interface for prompt workflow design
Version control for prompt iterations
Secure API key management
Collaborative environment for team work
Prompt branching and response manipulation
Model selection and template saving
Export options in JSON or YAML format
Advantages
Simplified prompt chaining
Visual design and testing of workflows
Built-in version control
Secure API key storage
Collaborative environment
Efficient LLM workflow management
Reduces time to deployment
缺点：Requires JavaScript to run
缺点：May have a learning curve for new users
缺点：Pricing information not explicitly provided (beta may be free but future pricing is unknown)
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30-Day Click Trend
08-22
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Prompt Picker helps you choose the best system prompts for your LLM applications.
Prompt Picker helps you choose better system prompts (aka custom instructions) for your LLM-powered applications by running experiments over simulated user inputs. In other words, it helps answer "...but which *exact* system prompt should I be using?"
