The article argues that the true value in working with large language models lies not in prompt engineering, but in context engineering. Since LLMs have no inherent memory between API calls, the continuity and knowledge users perceive are assembled by external systems. The author posits that the prompt is disposable, while the curated context provided around it is the actual product. This shift in focus highlights the challenge of effectively utilizing large context windows, as information buried in the middle can be lost, making curation and relevance crucial over simply increasing context size. AI
IMPACT Shifts focus from prompt crafting to context curation, highlighting the challenge of information retrieval in large context windows.
RANK_REASON Opinion piece discussing the evolution of LLM interaction paradigms.
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