This post compiles frequently asked questions from instructors Shreya and the author regarding AI evaluation principles, based on their experience teaching over 700 engineers and product managers. They offer opinions rather than universal truths, urging careful judgment in application.
Key questions addressed include:
- RAG’s Relevance: Despite claims of "RAG is dead," the core principle—using retrieval to enhance LLM outputs—remains vital; the focus should shift from abandoning retrieval systems to optimizing them.
- Model Selection and Evaluation: It’s often more effective to analyze errors than to hastily switch models; the same model can serve both task and evaluation efficiently.
- Custom Tools: Creating tailored annotation tools significantly enhances workflow usability, while binary evaluations often yield clearer insights than Likert scales.
Overall, the authors advocate for a structured, iterative approach to AI evaluation, emphasizing error analysis and tailored evaluation strategies. Readers are encouraged to join their final AI Evals course, offering a discount for attendees.