Hegel AI offers an open-source platform, PromptTools, for developing and monitoring large language model applications, enabling teams to experiment with prompts, models, and retrieval pipelines. The platform facilitates real-time evaluation and improvement of generative AI systems through human-in-the-loop feedback and custom metrics, enhancing performance and reliability in production environments.
Funding
$600K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
OSFounders
Product
Problem
Developing and deploying large language model (LLM) applications requires extensive experimentation and monitoring to ensure optimal performance and reliability. Teams need tools to efficiently test prompts, models, and retrieval pipelines, as well as to gather feedback and track performance metrics in real-time.
Solution
Hegel AI offers PromptTools, an open-source platform designed to streamline the development and monitoring of LLM applications. PromptTools enables teams to experiment with various prompts, models, and retrieval pipelines, facilitating rapid iteration and optimization. The platform supports real-time evaluation of generative AI systems through human-in-the-loop feedback, automated LLM evaluations, and custom metrics. By providing a comprehensive suite of tools for experimentation, monitoring, and evaluation, PromptTools helps teams enhance the performance and reliability of their LLM applications in production environments.
Target Audience
The primary users are developers and teams building large language model applications across various industries and company sizes.
Features
- Open-source SDK and playground for developing prompts, models, and pipelines
- Real-time monitoring of LLM systems in production
- Tools for gathering custom metrics and user feedback
- Human-in-the-loop annotation of responses
- Automated LLM evaluations
- Ability to run evaluation functions in code
- Integrations with various LLMs, vector databases, and frameworks