Phase 3: LLM Architecture · ~90 minutes · Python
Evaluation: Benchmarks, Evals, LM Harness
Goodhart's Law: when a measure becomes a target, it ceases to be a good measure. Every frontier lab games benchmarks. MMLU scores go up while models still can't reliably count the number of R's in "strawberry." The only eval that matters is YOUR eval -- on YOUR task, with YOUR data.
Hiring signal: Understanding of evaluation: benchmarks, evals, lm harness internals
What you will learn
- Build a custom evaluation harness that runs multiple-choice and open-ended benchmarks against a language model
- Explain why standard benchmarks (MMLU, HumanEval) saturate and fail to differentiate frontier models
- Implement task-specific evals with proper metrics: exact match, F1, BLEU, and LLM-as-judge scoring
- Design a custom evaluation suite targeting your specific use case rather than relying solely on public leaderboards
Introduction
Type: Build Languages: Python Prerequisites: Phase 10, Lessons 01-05 (LLMs from Scratch) Time: ~90 minutes
Learning Objectives
- Build a custom evaluation harness that runs multiple-choice and open-ended benchmarks against a language model
- Explain why standard benchmarks (MMLU, HumanEval) saturate and fail to differentiate frontier models
- Implement task-specific evals with proper metrics: exact match, F1, BLEU, and LLM-as-judge scoring
- Design a custom evaluation suite targeting your specific use case rather than relying solely on public leaderboards
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