RagaAI Catalyst
The RagaAI Catalyst software facilitates the observation, debugging, and evaluation of Agentic AI systems by providing tools for synthetic data creation, prompt testing, trace recording, and human feedback-based finetuning.
Description for RagaAI Catalyst
A comprehensive platform, RagaAI Catalyst is intended to facilitate the observation, evaluation, and debugging of Agentic AI workflows. It offers a variety of tools, including trace analysis, agent monitoring, experiment management, synthetic data generation, finetuning, and custom metric creation, to facilitate the deployment of high-quality AI.
Features of RagaAI Catalyst:
- Visualization and Monitoring: Offers a user-friendly interface that presents trace data and execution graphs for effective observability.
- Evaluation Tools: Evaluates the planning, memory retention, tool use, objective fulfillment, and safety checks in agent workflows.
- Experiment Management: Facilitates structured experiment monitoring through detailed run comparisons and analytics.
- Prompt Testing and Optimization: Facilitates the rapid testing, versioning, and evaluation of prompts to facilitate the efficient engineering of prompts.
- Synthetic Data and Finetuning: Enables the incorporation of human feedback to refine model outputs and provides synthetic data generation.
Use Cases for RagaAI Catalyst
- Agent Debugging: Utilizes comprehensive trace logging to identify and resolve issues in LLM calls, tools, and chat interactions.
- LLM Output Validation: Implements guardrails to guarantee precise and dependable responses by safeguarding AI.
- Prompt Engineering: Enhances the quality of responses by refining prompts and comparing LLM configurations.
- Custom Evaluation Development: Develops evaluation logic that is customized to meet the specific testing requirements of the client, utilizing Python and system prompts.
- Dataset Generation: Generates synthetic data that is context-aware and has customized schemas for the purpose of AI training and evaluation.
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