AI · Technology · 2026-09-09 13:33:00
This post summarizes the first chapter of AI Engineering by Chip Huyen, distinguishing ML engineering from AI engineering. Traditional ML engineering requires curating datasets and training models from scratch, while AI engineering adapts foundation models to specific use cases with lower barriers but challenges in evaluation and compute. It covers foundation model basics, LLM tokenization, masked vs. autoregressive models, and three adaptation pathways: prompt engineering, RAG, and fine-tuning, plus evaluation and cost optimization challenges.
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