Why LLM Patterns Are the Key to Enterprise Success — And Why Ignoring Them is a Mistake
Last Updated on November 3, 2024 by Editorial Team
Author(s): David Sweenor
Originally published on Towards AI.
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As the generative AI aura begins to fade, organizations realize that the technology is not the panacea they once thought. To move beyond tinker-toy prototypes to enterprise-grade AI systems is challenging, to say the least. McKinsey estimates that only 11% of organizations have adopted generative AI at scale.[1] Organizations are hoping to automate tasks, optimize processes, and improve overall productivity are excited about the potential. The excitement is certainly justified — AI can generate content, retrieve critical information, and summarize large corpora of data at a scale and speed unmatched by human labor. However, there’s a critical oversight many enterprise leaders are making: without a structured understanding of AI patterns, these initiatives are likely to fail.
Generative AI is not a one-size-fits-all solution. In order for AI to improve organizational productivity, it must be deployed using specific frameworks — AI patterns — that align the technology with the company’s use cases. There are five key AI patterns that, when applied correctly, offer a clear pathway to improving efficiency, decision-making, and automation: Author, Retriever, Extractor,… Read the full blog for free on Medium.
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