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
排序理由 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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