BiteSizedChunks.comLearn one small thing at a time.

Course contentsShow

795Introduction to Task-Specific Evaluation
796Classification Task Metrics
797Retrieval Quality Metrics
798Generation Quality Metrics
799Semantic Similarity Metrics
800Factuality and Hallucination Detection
801Instruction Following Metrics
802Task Completion and Success Rate
803Latency and Performance Metrics
804Domain-Specific Custom Metrics
805Multi-Dimensional Scoring
806Metric Selection Strategy
807What is LLM-as-a-Judge
808When to Use LLM-as-a-Judge
809Choosing the Judge Model
810Designing Evaluation Prompts
811Rubrics and Scoring Criteria
812Binary vs Scalar Judgments
813Comparative Evaluation (Pairwise)
814Chain-of-Thought for Judges
815Multi-Aspect Evaluation
816Judge Calibration and Validation
817Handling Judge Biases
818Cost and Latency Trade-offs
819What is Ground Truth and Why It Matters
820Creating Ground Truth from Historical Data
821Manual Annotation Workflows
822Domain-Specific Test Sets
823Sampling Strategies for Coverage
824Golden Datasets and Versioning
825Public Benchmarks and Adaptation
826Inter-Annotator Agreement
827Dataset Size and Statistical Power
828Continuous Ground Truth Updates
829What is a Regression Suite for LLM Systems
830Building Your First Regression Test Set
831Automating Regression Test Execution
832Defining Pass/Fail Criteria for Regression Tests
833Tracking Regression Test Results Over Time
834Production Monitoring: Key Metrics to Track
835Setting Up Alerts for Model Degradation
836Shadow Testing and Canary Deployments
837Continuous Evaluation with Production Traffic
838Maintaining and Evolving Your Regression Suite

795Introduction to Task-Specific Evaluation
796Classification Task Metrics
797Retrieval Quality Metrics
798Generation Quality Metrics
799Semantic Similarity Metrics
800Factuality and Hallucination Detection
801Instruction Following Metrics
802Task Completion and Success Rate
803Latency and Performance Metrics
804Domain-Specific Custom Metrics
805Multi-Dimensional Scoring
806Metric Selection Strategy
807What is LLM-as-a-Judge
808When to Use LLM-as-a-Judge
809Choosing the Judge Model
810Designing Evaluation Prompts
811Rubrics and Scoring Criteria
812Binary vs Scalar Judgments
813Comparative Evaluation (Pairwise)
814Chain-of-Thought for Judges
815Multi-Aspect Evaluation
816Judge Calibration and Validation
817Handling Judge Biases
818Cost and Latency Trade-offs
819What is Ground Truth and Why It Matters
820Creating Ground Truth from Historical Data
821Manual Annotation Workflows
822Domain-Specific Test Sets
823Sampling Strategies for Coverage
824Golden Datasets and Versioning
825Public Benchmarks and Adaptation
826Inter-Annotator Agreement
827Dataset Size and Statistical Power
828Continuous Ground Truth Updates
829What is a Regression Suite for LLM Systems
830Building Your First Regression Test Set
831Automating Regression Test Execution
832Defining Pass/Fail Criteria for Regression Tests
833Tracking Regression Test Results Over Time
834Production Monitoring: Key Metrics to Track
835Setting Up Alerts for Model Degradation
836Shadow Testing and Canary Deployments
837Continuous Evaluation with Production Traffic
838Maintaining and Evolving Your Regression Suite

← AI Engineering

Lesson 820 of 1,886·20. Evaluation Frameworks for LLM SystemsPro lesson

Creating Ground Truth from Historical Data

Extracting and labeling ground truth examples from production logs, past conversations, and user interactions.

This lesson is for subscribers

You've completed the free preview. Subscribe to unlock every lesson in every course.

See pricing Back to course