Counting API Error Rates

Debugging Failed LLM Generations

Why LLM Observability Is Hard

What LLMOps Is vs MLOps

Session and Conversation-Level Observability

Privacy and PII in LLM Logs

Prompt-to-Production Feedback Loop

Eval Datasets from Production Traces

A/B Testing Prompts and Models

Prompt Versioning and Experiment Tracking

LLM Observability Tools

Spans and Trace Structure

Correlating Traces with Feedback

Online vs Offline Evaluation

Observability vs Monitoring

Dashboards for LLM Metrics

Monitoring Hallucination Rate

CI/CD for LLM Applications

Golden Datasets for Evaluation

Prompt Versioning and Management

Cost Attribution

Capturing User Feedback Signals

What LLMOps Is

Sampling and Trace Retention

PII Handling in Logs

A/B Testing Prompts and Models

Production Quality Monitoring

Alerting on Anomalies

Detecting Model or Prompt Drift

LLM Observability Platforms and Tools

Dashboards for LLM Metrics

Alerting and Anomaly Detection for LLMs

Detecting Drift in LLM Systems

Detecting Hallucinations in Production

Capturing User Feedback Signals

LLM-as-a-Judge Quality Scoring

Online vs Offline Evaluation

Quality and Output Monitoring in Production

Token Usage and Cost Tracking

Logging Prompts, Completions, and Metadata

Three Pillars of LLM Observability

Logging Prompt Template Version

Measuring Token Throughput

Logging Cache Hit vs Miss

Counter and Histogram for LLM Metrics

Logging Thumbs Up/Down Feedback

Automated Eval Scoring on Logged Outputs

Sampling a Fraction of Traffic

Alerting on Latency/Cost Thresholds

Attaching Session ID to Requests

Capturing Tool Call Events in Traces

Recording Model Metadata

Wrapping Calls with a Decorator

Structured Logging with JSON Fields

Logging LLM Request and Response

Measuring End-to-End Latency

Correlation ID Across Steps

Instrumenting Spans for a Chain

A/B Testing Prompts and Models

Evaluation-Driven Feedback Loops

CI/CD for Prompts and LLM Apps

Alerting, Dashboards, and SLOs

Prompt and Model Versioning

Sampling and PII in Logs

Detecting Hallucinations and Regressions

Tracing Requests and Spans

Logging Prompts, Completions, Metadata

Online vs Offline Evaluation

LLM Observability Lifecycle

Error Rate and Failure Monitoring

Capturing User Feedback Signals

LLM Observability Platforms and Tools

What LLMOps Is vs MLOps

Distributed Tracing for Chains and Agents

Cost Tracking and Budget Alerting

Detecting Model, Data, and Prompt Drift