If you are completely new to product-specific LLM evals (not foundation model benchmarks), see these posts: part 1, part 2 and part 3. Otherwise, keep reading.
Motivation Iterating Quickly == Success
Case Study: Lucy, A Real Estate AI Assistant The Types Of Evaluation - Level 1: Unit Tests
- Level 2: Human & Model Eval
- Level 3: A/B Testing
- Evaluating RAG
Eval Systems Unlock Superpowers For Free - Fine-Tuning
- Data Synthesis & Curation
- Debugging
The Problem: AI Teams Are Drowning in Data Step 1: Find The Principal Domain Expert Step 2: Create a Dataset Step 3: Direct The Domain Expert to Make Pass/Fail Judgments with Critiques Step 4: Fix Errors Step 5: Build Your LLM as A Judge, Iteratively Step 6: Perform Error Analysis Step 7: Create More Specialized LLM Judges (if needed) Recap of Critique Shadowing Resources
How error analysis consistently reveals the highest-ROI improvements Why a simple data viewer is your most important AI investment How to empower domain experts (not just engineers) to improve your AI Why synthetic data is more effective than you think How to maintain trust in your evaluation system Why your AI roadmap should count experiments, not features
