Sometimes you'll need a prompt, and other times an agent workflow.
"You're probably making AI harder than it needs to be."
This advice from Corey Noles and Grant Harvey's latest episode of The Neuron podcast urges greater simplicity in what has become a complicated and confusing affair in recent years -- making generative AI fit for the organization.
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With 95% of generative AI projects failing, as found in a recent MIT study, it's time to put these initiatives into perspective.
Many people inside and outside the technology profession view implementing gen AI as a major task that requires rigorous project management discipline. Experts even refer to that phenomenon with ZDNET, telling people not to rush into AI, but to think carefully about what they are doing first.
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While some AI initiatives, at a base level, don't require such overthinking, others, on the other hand, do require more thoughtful planning. So, where does a budding AI professional draw the line?
Overall, people "are getting it all wrong" with implementing AI, Noles and Harvey asserted. Success is a matter of knowing when the simplest approaches apply -- using a screwdriver instead of applying a sledgehammer for a challenge that may require AI. It's also about knowing when more sophisticated approaches are needed to prevent AI projects from going off the rails.
The prodcasters suggested four ways to solve a problem with gen AI:
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"Start with the simplest tool that solves your problem," Noles advised. "Don't build an agent when a basic chat will do. We find people get super excited about all these awesome tools. But in practice, you really don't need to jump straight to building complex agents when a simple prompt works."
But also "don't spend two hours pulling your hair crafting the perfect prompt and trying to one-shot a complicated task when an agent could handle the whole workflow for you," he added. "There are definitely times you really do need to craft an epic prompt or build an agent workflow. The trick is knowing when to use what."
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Noles and Harvey provide some guidelines to help professionals decide how much effort an AI-related project needs:
Gen AI has the potential to advance productivity and innovation if employed smartly. Understanding the four stages of deployment and the effort required can prevent gen AI from becoming a counterforce to the productivity and innovation the technology is supposed to deliver.