Compiled AI Evaluation FAQs: Key Lessons from Our Course
This post gathers the most frequently asked questions from our AI evaluation course, providing clear, actionable answers to common challenges in LLM and AI system evaluation. Topics include model selection, annotation tooling, evaluation metrics, debugging, synthetic data generation, and best practices for resource allocation. The FAQ aims to help practitioners make informed decisions about building, testing, and improving AI systems, with practical advice for both technical and strategic aspects of evaluation
1. AI Evaluation Course: Compiled FAQ
Compiled AI Eval FAQs from our course into this blog post https://lnkd.in/gb_nJmXG
What else would you like to see @sh_reya & I answer? AMA
Q: Can I use the same model for both the main task and evaluation? Q: How much time should I spend on model selection? Q: Should I build a custom annotation tool or use something off-the-shelf? Q: Why do you recommend binary (pass/fail) evaluations instead of 1-5 ratings (Likert scales)? Q: How do I debug multi-turn conversation traces? Q: Should I build automated evaluators for every failure mode I find? Q: How many people should annotate my LLM outputs? Q: What gaps in eval tooling should I be prepared to fill myself? Q: What is the best approach for generating synthetic data? Q: How do I approach evaluation when my system handles diverse user queries? Q: How do I choose the right chunk size for my document processing tasks? Q: How should I approach evaluating my RAG system? Q: What makes a good custom interface for reviewing LLM outputs? Q: How much of my development budget should I allocate to evals?