Debugging slow or expensive LLM calls requires specialized observability tools beyond standard APM metrics. Key factors to monitor include token counts, per-model costs, guardrail overhead, and detailed prompt-level information. Traces should incorporate input/output tokens, cost, and latency for each hop, ideally feeding into existing platforms like Langfuse or SigNoz via OpenTelemetry. Guardrail performance, such as blocking rates and added latency, also warrants separate tracking to manage operational expenses. AI
IMPACT Enhanced LLM observability tools help developers optimize costs and performance, crucial for efficient AI application development.
RANK_REASON The article discusses tools and techniques for LLM observability, focusing on specific software and metrics.
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