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Learning Topics
This roadmap covers the following topics:
✅ 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
✅ 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
⚪ 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
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