According to a recent survey, only 30.1% of teams deploying LLMs have implemented observability despite large majorities wanting better debugging workflows and ways to tackle hallucinations, toxicity, and other issues. As teams play catch-up, what should they look for when assessing an LLM observability platform? Informed by experience working with hundreds of practitioners across dozens of large enterprises and technology companies with LLM apps in production, this checklist covers essential elements to consider when evaluating an LLM observability provider.
Dive into details on essentials like:
- LLM System Evaluations
- LLM Traces and Spans
- Prompt Engineering
- Retrieval Augmented Generation
- Fine-Tuning
- Embeddings Analysis
- Platform Support
Read the Checklist
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