LLM Observability
LLM observability is the practice of monitoring, tracing, and evaluating the behavior of large language models during inference and across system pipelines, to ensure:
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Performance (latency, uptime)
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Correctness (output quality, factuality)
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Safety (toxicity, hallucinations)
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Explainability (understanding how/why a response was generated)
Benefits
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Faster debugging of bad generations
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Trust & safety through toxic/harmful content detection
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System optimization by analyzing latency, tool usage
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Regulatory compliance via traceability and audit logs
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Better user experience by tuning prompts or chains based on evals
LLM Observability
1
Monitoring
What to Monitor:
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Latency & throughput: Time taken to generate a response.
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Token usage: Input/output token counts and costs.
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Model health: Timeouts, failures, token rate limits.
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User behavior: Query patterns, retry loops, dissatisfaction signals.
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Abuse detection: Prompt injections, jailbreak attempts.
Tooling & Implementation:
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Set up dashboards (e.g., in Grafana, Datadog) for latency, cost, usage metrics.
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Log inputs/outputs, token counts, and rate-limit errors.
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Integrate with APIs from OpenAI, Anthropic, or open-source models (e.g., LangChain, LlamaIndex).
2
Tracing
What to Monitor:
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Latency & throughput: Time taken to generate a response.
-
Token usage: Input/output token counts and costs.
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Model health: Timeouts, failures, token rate limits.
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User behavior: Query patterns, retry loops, dissatisfaction signals.
-
Abuse detection: Prompt injections, jailbreak attempts.
Tooling & Implementation:
-
Set up dashboards (e.g., in Grafana, Datadog) for latency, cost, usage metrics.
-
Log inputs/outputs, token counts, and rate-limit errors.
-
Integrate with APIs from OpenAI, Anthropic, or open-source models (e.g., LangChain, LlamaIndex).
3
Evaluations (Evals)
What to Monitor:
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Latency & throughput: Time taken to generate a response.
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Token usage: Input/output token counts and costs.
-
Model health: Timeouts, failures, token rate limits.
-
User behavior: Query patterns, retry loops, dissatisfaction signals.
-
Abuse detection: Prompt injections, jailbreak attempts.
Tooling & Implementation:
-
Set up dashboards (e.g., in Grafana, Datadog) for latency, cost, usage metrics.
-
Log inputs/outputs, token counts, and rate-limit errors.
-
Integrate with APIs from OpenAI, Anthropic, or open-source models (e.g., LangChain, LlamaIndex).
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