Eugene Yan presented key learnings from building with Large Language Models (LLMs) at the AI Engineer World's Fair 2024. The keynote, co-authored with others, focused on practical aspects of LLM system development, including evaluations, Retrieval-Augmented Generation, and guardrails. Yan also discussed challenges in consistently evaluating LLMs, citing concerns raised by researchers at OpenAI, Anthropic, and others regarding benchmark reliability and task relevance. AI
RANK_REASON The content is a presentation and reflection on practical LLM engineering, drawing from prior writings and community feedback, rather than a new model release or significant industry event.
- AI Engineer Summit
- AI Engineer World's Fair 2024
- Anthropic
- CNN/Daily Mail
- GitHub
- InstructGPT
- LLMs
- MMLU
- OpenAI
- Thomas Dohmke
- Eugene Yan
- Swyx
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