Why LLM Patterns Are the Key to Enterprise Success — And Why Ignoring Them is a Mistake

Author(s): David Sweenor

Originally published on Towards AI.

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In nature, patterns are everywhere — you just have to look. Photo by author David E. Sweenor

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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Published via Towards AI