Level 6 Complete
Level 6 complete! You’ve built a complete eval pipeline — from your first Evalite eval through deterministic scorers to LLM-as-Judge and production monitoring with Langfuse. You can now systematically measure, compare, and improve LLM outputs. That’s eval-driven development — a skill most AI engineers learn late in their journey.
What You Learned
Section titled “What You Learned”- Evalite Basics: The TypeScript-native eval framework with
data,task, andscorers—.eval.tsfiles,traceAISDKModelfor AI SDK integration, dashboard at localhost:3006 - Deterministic Eval: Fast, cheap scorers without an LLM — inline scorers,
createScorerfor reusability, Levenshtein from the Autoevals library, graduated scores (0-1) - LLM-as-a-Judge: An LLM evaluates the output of another — Factuality scorer with
generateObjectand Zod schema, score scale (A-E), rationale for traceability - Dataset Management: Representative, diverse test data with 20-50 cases for development — systematically covering categories, including edge cases, dataset critiquing with LLM
- Langfuse: Production observability for LLM applications — traces, generations, scores, cost monitoring. Evalite for development, Langfuse for production
Updated Skill Tree
Section titled “Updated Skill Tree”
Next Level
Section titled “Next Level”Level 7: Streaming — How do you deliver LLM answers to the user in real time? You’ll learn stream events, partial updates, and how to build streaming UIs that feel like the LLM is typing live.