What is LLM Observability?

As LLMs are embedded into user-facing apps, observability becomes critical. Traditional monitoring tools weren’t designed for the unique behaviors of generative AI—such as probabilistic output, long conversations, or agent chaining.

LLM observability includes:

• Logging all prompts and responses
• Flagging unsafe or unexpected outputs
• Monitoring latency, token usage, and cost
• Tracking interactions across multiple models or agents
• Surfacing risk indicators (e.g., jailbreaks, hallucination, data leakage)

Observability tools help teams:

• Debug user issues and output problems
• Validate model responses for safety and reliability
• Detect abuse or misuse in real time
• Inform finetuning or guardrail adjustments

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