Llm Prompt Debugger
Clean UI for LLM development workflows with prompt versioning and model selection. Built for engineers, not hype. Streamlined prompt → model → tag → export workflow. Currently supports OpenAI, Claude, and Ollama.
Overview
The LLM Prompt Debugger presents an engaging platform for those immersed in evaluating and labeling outputs from various Language Learning Models (LLMs). It serves as a comprehensive tool for both developers and analysts, allowing users to enhance their productivity by assessing and refining LLM interactions seamlessly. Whether you're looking to improve your prompts or analyze responses from different models, this tool is set up to meet your specific needs in an accessible manner.
The tool supports multiple models such as OpenAI, Claude, and Ollama, making it versatile for users with different preferences. By incorporating features that foster collaboration and ease of use, this platform stands out for anyone involved in the realm of language-based AI solutions.
Features
- Prompt Input + Response Viewing: Easily input prompts and view corresponding model responses for quick evaluation.
- Model Selection: Choose from popular models including OpenAI, Claude, and Ollama for diverse LLM testing.
- Tagging UI for Prompt Categorization: Utilize a dynamic tagging system to apply meaningful categories to each prompt-response pair, enhancing organization.
- Export Support: Export your analysis in JSON for programming purposes or Markdown for documentation and knowledge sharing.
- Hotkey Functionality: Quickly run prompts with simple keystrokes (Cmd+Enter or Ctrl+Enter) for a more efficient workflow.
- Semantic and Stylistic Tagging System: Add built-in tags for various tones and purposes or create custom tags to suit your specific needs.
- Deployment Flexibility: Supports various deployment methods, including Vercel, Netlify, and Docker, making it adaptable to different environments.
- Community Engagement: Contributing and collaboration are encouraged, with options to submit PRs or engage in discussions for continual improvement.
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