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AI Observability, : - Coggle Diagram
AI Observability
What it is
LLM Observability is the process of monitoring, tracing, and analyzing the behavior of Large Language Models during inference and interaction.
It provides visibility into model performance, prompt-response behavior, latency, and resource usage.
Combines principles from traditional application observability (metrics, logs, traces) with AI-specific telemetry.
Enables teams to understand how models behave in production, not just during training.
Often integrates with tools like OpenTelemetry, API gateways, or custom SDKs to collect model telemetry data.
Observing the LLM from the perspectives of performance, drift, security, and responsibility
Why it is needed
To detect anomalies, biases, and hallucinations in LLM outputs in real time.
To optimize latency, cost, and throughput across different LLM deployments (local or API-based).
To ensure reliability, transparency, and accountability in AI-driven systems.
To comply with governance and security policies, preventing data leakage or misuse.
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What it does
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Captures telemetry data — metrics (response time, token usage), traces (API calls), and logs (prompt/response history).
Alerts or automates responses when anomalies, drift, or policy violations are detected.
How it is done
Detect sensitive and malicious patterns by performing PII and prompt injection analysis on both prompts and responses.
Extract meaningful insights from the collected telemetry, aligning with the four observability pillars — logs, metrics, traces, and security.
Collect telemetry data from user–LLM and client–LLM conversations to capture complete interaction context.
Store and centralize all metrics, traces, and logs for long-term observability, analytics, and visualization.
Monitor system performance by identifying latency spikes and throughput bottlenecks that indicate degradation.
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