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

Detecting Model or Prompt Drift

Capturing User Feedback Signals

Correlating Traces with Feedback

PII Handling in Logs

A/B Testing Prompts and Models

Online vs Offline Evaluation

Observability vs Monitoring

Spans and Trace Structure

LLM Observability Tools

Production Quality Monitoring

Prompt Versioning and Management

Sampling and Trace Retention

Cost Attribution

Golden Datasets for Evaluation

CI/CD for LLM Applications

Monitoring Hallucination Rate

Dashboards for LLM Metrics

Alerting on Anomalies

What LLMOps Is

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

Automated Eval Scoring on Logged Outputs

Counter and Histogram for LLM Metrics

Sampling a Fraction of Traffic

Alerting on Latency/Cost Thresholds

Attaching Session ID to Requests

Capturing Tool Call Events in Traces

Logging Cache Hit vs Miss

Logging Thumbs Up/Down Feedback

Instrumenting Spans for a Chain

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

A/B Testing Prompts and Models

Alerting, Dashboards, and SLOs

CI/CD for Prompts and LLM Apps

Prompt and Model Versioning

Sampling and PII in Logs

Evaluation-Driven Feedback Loops

Tracing Requests and Spans

Cost Tracking and Budget Alerting

Detecting Model, Data, and Prompt Drift

Online vs Offline Evaluation

LLM Observability Platforms and Tools

What LLMOps Is vs MLOps

Distributed Tracing for Chains and Agents

Detecting Hallucinations and Regressions

Capturing User Feedback Signals

Logging Prompts, Completions, Metadata

Error Rate and Failure Monitoring

LLM Observability Lifecycle