Observability

AI observability and evaluation suite

Log all interactions for easy monitoring, use large-scale LLM quality evaluations, and leverage advanced tools for assessing AI-generated text, optimizing your RAG pipeline efficiently

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Immediate insight and enhanced debugging capabilities

Live metrics tracking

Monitor key performance indicators such as response times, system throughput, and error rates in real-time. Instantly see code change effects or traffic fluctuations.

Integrated debugging

Quickly pinpoint and resolve errors with tools that highlight inefficient code paths and resource bottlenecks.

Custom alerts

Configure alerts for critical issues or metric thresholds to maintain optimal performance without manual oversight.

Quality evaluation at scale

Precision assessment for LLM outputs

  • Scalable quality checks: automatically evaluate output quality from fluency and relevance to accuracy, supporting both small-scale tests and large-scale deployments.
  • Performance benchmarking: continuously compare your LLM’s outputs against established standards or previous versions to ensure consistent improvement.
  • Comprehensive metrics suite: utilize a broad set of evaluation metrics to thoroughly assess LLM performance and identify areas for enhancement.

Detailed logging

Complete transparency with comprehensive logs

  • All Interactions logged: maintain detailed records of all requests and their corresponding outputs to simplify monitoring and troubleshooting processes.
  • Accessible log interface: easily search and filter logs by various criteria including date, error occurrence, and more to quickly locate necessary data.
  • Secure long-term storage: keep logs safe and accessible to comply with audit requirements and support in-depth historical analysis.

Advanced evaluation tools

Cutting-edge methods for refined insight

  • LLM-as-judge evaluations: employ LLM-as-judge techniques for rapid feedback on model outputs, providing a quick measure of text quality and model reliability.
  • Research-based tools: leverage the latest findings in AI research to utilize up-to-date tools for evaluating your LLMs, especially within your RAG pipeline.
  • RAG pipeline analysis: gain specific insights into how retrieval techniques impact the effectiveness of your LLM outputs, enabling targeted improvements.