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The Prompt Engineer Is Fit for the Enterprise
In the AI landscape, I stand firm in my belief that prompt engineering is a potent asset for enterprises. The power of large language models (LLMs) like ChatGPT, GPT3, and GPT4 is undeniable. Although not tailored to build enormous systems like Twitter due to their inherent limitations, they excel when applied to tasks not necessitating massive scale. The key, however, lies in mastering the art of asking the right questions and adopting a test-and-learn approach.
My conviction is rooted in my personal experience within the enterprise realm, where I’ve observed the transformative power of LLMs. When harnessed strategically, these models can significantly boost productivity and expedite results. But it all comes down to just-in-time learning — a skill that revolves around how and what we ask these models.
Allow me to bring my professional life into the picture. As a data analyst, I facilitate data analytics projects, design apps and dashboards for better data accessibility, and provide ad hoc analysis of business metrics to generate actionable insights. My tasks vary from querying databases, crafting data marts, building dashboards, to writing comprehensive reports.
