LLM as Judge: Reproducible Evaluation for LLM Systems - Learning Roadmap | Nemorize

✅ Why Rigorous Eval Exists

• ⚪ Classical Metrics Failed
  • ⚪ BLEU and ROUGE Failure Cases
  • ⚪ Human Eval Doesn't Scale
• ⚪ Cost-of-Being-Wrong Framework
  • ⚪ Defining Cost Tiers
  • ⚪ Eval as an Architecture Decision

✅ The LLM Judge Premise

• ⚪ Where LLM Judges Shine
  • ⚪ Strengths With Evidence
  • ⚪ Where LLM Judges Struggle
• ⚪ The Right Tool Decision
  • ⚪ Judge vs. Metric vs. Pipeline

✅ Core Judging Patterns

• ✅ Rubric Design and Criteria Decomposition
  • ⚪ Atomic Criteria vs. Holistic Rubrics
  • ⚪ Chain-of-Thought Scoring
  • ⚪ Rubric Drift
• ⚪ Pointwise, Pairwise, and Reference-Based Modes
  • ⚪ Pointwise Scoring and Its Biases
  • ⚪ Pairwise Comparison and Position Bias
  • ⚪ Reference-Based Judging

✅ G-Eval and Structured Output

• ✅ G-Eval (2026): Architecture and Variants
  • ⚪ Token Probability Scoring
  • ⚪ FActScoring and Fact Decomposition
• ⚪ Structured Output for Judges
  • ⚪ Schema Design for Eval Payloads
  • ⚪ Constrained Decoding and Tool-Use Patterns

✅ Systematic Failure Modes

• ⚪ Self-Preference and Verbosity Bias
  • ⚪ Detecting Self-Preference
  • ⚪ Verbosity Bias in Practice
• ⚪ Bias-to-Mode Mapping
  • ⚪ Position Bias Measurement
  • ⚪ Rubric Drift Over Time

⚪ The Hybrid Pattern: Extraction Plus Deterministic Rules

• ⚪ Why Split Extraction from Judgment
  • ⚪ Designing the Fact Schema
  • ⚪ Extraction Failure Modes
• ✅ Deterministic Scoring: Rules, Trees, and DAGs
  • ⚪ Encoding Rubrics as Rules
  • ⚪ Audit Trails and Reproducibility
  • ⚪ When the Hybrid Pattern Is Overkill
• ✅ Datalog for Deterministic Scoring
  • ✅ Inducing Datalog Rules for Reproducible LLM Evaluation

⚪ Meta-Evaluation and Production

• ⚪ Meta-Evaluation: Testing the Judge
  • ⚪ Human Correlation and Benchmark Suites
  • ⚪ Adversarial Test Cases
• ⚪ Production: Cost, Latency, and Drift
  • ⚪ Cost Architecture and Model Tiering
  • ⚪ Drift Monitoring and Judge Maintenance
  • ⚪ When LLM-as-Judge Is the Wrong Tool

⚪ Building Your Eval Stack

• ⚪ CI, Nightly, and Audit Pipeline Design
  • ⚪ What Goes in Each Layer
  • ⚪ The Eval Stack Decision Framework
• ⚪ Putting It Into Production
  • ⚪ Eval as Living Infrastructure
  • ⚪ From Demo to Defensible System