Eugene Yan's article explores how to align specific Large Language Model (LLM) patterns with various problems encountered in their application. He categorizes LLMs into external, which offer state-of-the-art quality but have limitations, and internal, which provide more control but often lag in performance. The piece details how patterns like evals, fine-tuning, RAG, and user feedback can address issues such as poor performance, data constraints, and the need for task-specific metrics. AI
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RANK_REASON The article is an opinion piece by a named author discussing patterns and problems related to LLM usage.