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AI Engineering requires a shift in mindset
In the world of traditional software development, experience breeds intuition. Engineers learn the ropes, becoming adept at gauging what's feasible and what's not. The uncertainties usually dance around quality, costs, and performance, rarely feasibility. But with Generative AI, the rulebook gets tossed out the window. It's a new frontier where even seasoned engineers find themselves back at square one, trying to figure out the bounds of what's possible.
One might attribute this to the novelty of it all. Groundbreaking models like GPT-4 are barely a year old, and everyone's still getting their feet wet. But there's more to it. The black-box nature of modern AI systems, with their stochastic behaviors, brings a level of unpredictability that's both exciting and challenging. It's like venturing into a dense forest with a compass but no map.
Now, the conversation extends beyond just building a product to understanding and unraveling what we've built. It's a whole new ballgame. Foundation models are like raw, uncharted lands waiting to be explored. They are "few-shot learners," able to generalize and transfer knowledge beyond what their creators intended. It's almost like having a conversation with these models to discover their capabilities.
This unpredictable nature of Generative AI has birthed new roles in the tech world. We already have ML Researchers and ML Engineers. Now, we have AI Engineers, a new breed who deal with extracting value out of foundation models. It's a shift from the nitty-gritty of algorithm training to exploring the vast capabilities of pre-built models.
The term "Prompt Engineer" was floated around, but it barely scratches the surface of what AI Engineers do. It's not just about prompting; it's about orchestrating a symphony between the code, the model, and the unknown potential waiting to be unlocked.
But with all this excitement comes a sobering reality. The unpredictability means that products built on Generative AI carry a cloud of uncertainty. Even with a robust model, there's no telling if the product will hit the mark. The feasibility check is replaced by a series of exploratory sprints to understand what the AI can do and how it aligns with market needs.
In essence, AI Engineering is not just a shift in technology; it's a shift in mindset. It demands a spirit of exploration, a willingness to embrace the unknown, and a relentless pursuit to decode the capabilities of Generative AI. It's about navigating through a maze where the walls keep shifting, but the potential reward at the end is a treasure trove of innovative products that could redefine the digital landscape.