AI Fluency Series #1: Delegation - Why the “Hammer and Nail” Trap Slows Us Down
“AI can do everything — right?” That’s the illusion I see too often in workplaces today. People hand over tasks to LLMs with vague prompts, expecting instant brilliance. The reality? Without human guidance, careful problem framing, and smart task delegation, even the most advanced AI can stumble — sometimes spectacularly.
One of the biggest mistakes I see with people delegating work to AI is that they skip the first step: defining the problem. They throw an abstract, half-baked prompt at a model and expect brilliance to come out. Unsurprisingly, it doesn’t. Without a clear problem statement, everything downstream gets shaky.
This lack of clarity leads to a second failure: not breaking the problem into smaller tasks. Problem decomposition isn’t just project management jargon — it’s how you figure out what you should do yourself and what you can confidently offload to an AI. Breaking a problem into smaller tasks might seem obvious, but it hasn’t been obvious in project management for the majority of companies for years. Countless books have been written on this topic, and walk into almost any company and you’ll hear teams complaining about it. I find it a little humorous that a practice humans have struggled to master for decades is now showing up as a source of frustration in our work with AI. Without that step, you’re just handing over a messy puzzle and hoping the machine magically solves it.
And here’s the final link in the chain: not all LLMs are good for all tasks. For now, each has strengths and weaknesses — some are fast but shallow, others are slow but precise, some are generalists, others specialists. Picking the right one matters.
But because LLMs are so easy to use, people fall into the trap of thinking they’re universal. It’s the old line: if all you have is a hammer, everything looks like a nail. The danger is that the “hammer” in this case looks shiny, smart, and confident — but using the wrong model on the wrong problem wastes time and builds frustration.
Take KPMG’s TaxBot as an example. They didn’t just throw a one-liner at ChatGPT and hope for the best — they wrote a 100-page prompt to train it on the ins and outs of tax law. That’s the difference between blind delegation and thoughtful design. It’s not glamorous, but it shows that if you want an AI to do serious work, you have to give it serious preparation.
It’s not just prompt trickery —memes about the “10 best ChatGPT prompts” may get clicks, but real success with LLMs demands a deeper, systematic approach: problem awareness, platform awareness, and task delegation.
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Problem awareness means deeply understanding the problem you're trying to solve—knowing its scope, constraints, and stakes—not just typing a question and hoping for the best.
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Platform awareness means knowing your tools. Each LLM has its quirks: some need chain-of-thought prompting, others thrive with persona-based prompts. What works for GPT-4 might fail with PaLM 2.
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Task delegation is about matching each micro-task to the right model or technique—breaking the problem into subtasks and deciding what humans do versus what goes to AI.
This is not a “prompt hack.” It’s more like software craftsmanship. In practice, LLM engineering is evolving into something like a mini discipline of its own: combining clarity, modularity, iteration, version control, and even libraries of prompt patterns.
In the end, successful AI delegation isn’t about flashy prompts or jumping straight to solutions. It’s about humans leading the process : thinking clearly, breaking problems down, and choosing the right tools. LLMs are powerful, but they have limitations, and without careful guidance from us, they can easily misfire. True productivity comes from a thoughtful partnership between human insight and AI capability.
Sources and further reading:- Lesson 4: A closer look at Delegation | AI Fluency: Framework & Foundations Course
- KPMG wrote 100-page prompt to p.fst-italic.text-muted.border-top.pt-3.mt-4