PromptTools
Open-Source Toolkit for Building and Optimizing LLM Applications
Description
PromptTools, developed by Hegel AI, is an open-source software development kit (SDK) and playground tailored for teams building applications using large language models (LLMs). It provides a comprehensive suite of tools to streamline the development lifecycle, from initial experimentation to production monitoring and continuous improvement.
The platform empowers users to systematically develop and test prompts, models, and retrieval pipelines through structured experiments. It includes capabilities for monitoring LLM systems in production environments, allowing teams to gather custom metrics and understand real-world performance. PromptTools offers versatile evaluation methods, including automated checks using LLMs, custom evaluation functions executed in code, and human-in-the-loop annotation for nuanced feedback. This feedback loop is integral for refining prompts and enhancing overall application quality over time. It supports integration with a wide range of LLMs, vector databases, and popular development frameworks.
Key Features
- Experimentation Engine: Develop and compare prompts, models, and retrieval pipelines.
- Production Monitoring: Track LLM systems live and gather custom performance metrics.
- Multi-faceted Evaluation: Assess responses using automated (LLM-based), code-based, and human-in-the-loop methods.
- Feedback Integration: Use evaluation results and feedback to iteratively improve prompts.
- Open-Source SDK & Playground: Access tools via Python code, notebooks, or a user-friendly playground.
- Wide Integrations: Connects with popular LLMs, vector databases, and frameworks (e.g., LangChain).
- Response Annotation: Facilitates human review and labeling of model outputs for evaluation.
Use Cases
- Developing and testing prompts for LLM applications.
- Comparing performance across different LLM models or versions.
- Building and evaluating retrieval-augmented generation (RAG) systems.
- Monitoring the behavior and cost of LLM applications in production.
- Evaluating the quality, safety, and accuracy of LLM responses.
- Optimizing prompts iteratively based on collected data and feedback.
- Collaborative development and refinement of prompts within a team.
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