What is Hegel AI?
The system facilitates evaluation using various methods, including human-in-the-loop annotation, LLM-based auto-evaluation, and custom evaluation functions within code. It supports integrations with numerous LLMs, vector databases, and frameworks to facilitate LLM application development across different industries and company sizes.
Features
- PromptTools SDK & Playground: Open-source tools for developing prompts, models, and pipelines with experiments.
- Production Monitoring: Monitor LLM systems in production and gather custom metrics.
- Feedback Integration: Use feedback to improve prompts over time.
- Multi-Approach Evaluation: Evaluate systems using human annotation, LLM auto-evaluation, and code functions.
- Wide Integrations: Supports integration with various LLMs, vector databases, and frameworks.
Use Cases
- Developing and testing prompts for LLM applications.
- Building complex LLM retrieval pipelines.
- Monitoring the performance and cost of LLM applications in production.
- Evaluating the quality of LLM responses.
- Iteratively improving LLM prompts based on evaluations and user feedback.
- Managing LLM application development workflows for teams.
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