A pragmatic guide to LLM evals for devs
8 days ago
- #Error Analysis
- #LLM Evals
- #AI Engineering
- Evals are essential for AI engineers to systematically improve AI quality, moving beyond guesswork.
- LLMs are non-deterministic, making traditional testing methods insufficient; evals help verify performance.
- Hamel Husain, an expert in AI evals, emphasizes the importance of error analysis and systematic workflows.
- The 'vibe-check development trap' occurs when changes to LLMs are made based on superficial assessments.
- Three 'gulfs' in LLM development: Comprehension, Specification, and Generalization, must be navigated.
- Error analysis involves open coding and axial coding to identify and prioritize failure modes.
- Code-based evals are used for deterministic failures, while LLM-as-judge handles subjective cases.
- Building an LLM-as-judge requires partitioning data to prevent memorization and validating against human expertise.
- Evals should be integrated into CI/CD pipelines and continuously monitored with production data.
- The 'flywheel of improvement' involves analyzing, measuring, improving, and automating processes iteratively.