Lazy Prompting
Everyone does it. But at what cost?
We've all seen the flashy OpenAI demos. A brilliant engineer types a simple prompt, and BOOM! Pure magic happens. The crowd “oohs” and “aaahhhs”.
The AI is framed as the genius, the oracle, the star of the show, while the person behind the question is relegated to a mere afterthought. But if we’re reduced to just typing a few simple words and watching the magic, aren’t we missing out on the real skill: the art of thinking and building a true AI thought partner?
I get the marketing angle. It makes AI feel effortless, almost mystical. Yet this narrative skews reality. It devalues the skill of crafting effective prompts and diminishes the role of the person doing the thinking. Worse still, it creates a stigma. If AI is supposed to be so smart, why should we have to put in the work? Why should we think at all?
Lazy Prompting: A Shortcut to Shallow Thinking
Lazy prompting happens when we give AI only the bare minimum of context. We all do it, but here are some all-too-common examples:
Email Chains: Paste an email thread into ChatGPT and prompt “write a response.” The result? A response that might be correct, but you’ve skipped the vital step of clarifying what you actually need.
Generic Strategies: Ask AI to “generate a marketing strategy” without specifying whether it’s for B2B or B2C, or outlining goals, client context, and everything else you could have considered. The output ends up being generic and unfocused.
Long Reports: Drop in a 50-page industry report and simply request “summarize this and give me key takeaways.” Without highlighting relevant sections or defining the type of insights you want, you’re likely to miss the nuances.
When we provide minimal input, we not only limit the AI’s potential but also shortchange our own thinking. Crafting a detailed prompt forces us to clarify our objectives, define the problem, and think critically. It turns the act of prompting into a cognitive exercise.
Thinking and Prompting Require Time
Research shows that writing by hand improves comprehension, retention, and problem-solving. Studies using brain imaging have found that handwriting activates more complex neural pathways than typing, engaging areas related to memory formation, motor coordination, and conceptual understanding.
While we don't need to do this for every task, we should always be considering ways to deepen our underrating of a task before moving to augmented workflows. This form of manual creation and ideation is so critical to creating great work. We can't lose it. Physically working with a problem forces a structured, slower engagement with the problem. It really helps our brains process it fully. So, for those big, knowledge intensive tasks, consider writing down your ideas first.
We should think of prompt structuring in a similar vein. When you open up a chat window, you're engaging in another layer of cognitive effort: structuring, sequencing, and providing necessary context. It's not prompt engineering, it's prompt thinking. 😉
This process requires careful thought about what your AI partner needs to 'know' to generate useful insights. By intentionally shaping your prompt, you refine your understanding of the task itself, uncovering gaps, sharpening priorities, and improving the AI's response quality.
You want to ensure that both you and the AI are aligned in a problem-solving process that is more creative, rigorous, and effective. Rather than simply instructing AI, you're actively refining your own thinking and forcing yourself to break down problems in a way that clarifies objectives, highlights constraints, and deepens overall comprehension. So take your time when you start that conversation.
More Detail, More Value: Even with Advanced Models
We are all steeped in the hype: AI Agents operate autonomously! Reasoning models surpass PhD.-level tasks!
This hype cycle makes it seem that more powerful AI models will require less input. But the opposite is true. Even the most sophisticated reasoning models like the shiny new o3 and o1 from OpenAI need the carefully constructed scaffolding to produce deep, nuanced outputs. I would argue they need more than the more basic models. After all, a Ferrari won’t get far in first gear.
Find the Right Balance
I don’t wish to to dismiss “lazy prompting” altogether. In fast-paced, iterative scenarios, starting with a simple prompt can kick off a creative brainstorming session. But for high-value, knowledge-intensive tasks, a detailed prompt is indispensable.
Ask yourself:
When does speed outweigh depth?
Can a minimal prompt sometimes spark unexpected creativity?
Also Consider:
Start by frontloading: Spend time creating a GPT, Project, or a chat window by injected as much context as you can. Then you can get away with more minimal prompts as you progress.
Go Multimodal: Use images, transcripts, and other inputs to continually direct the AI towards the task at hand.
Iterate: Refine these ideas with follow-up prompts to add clarity and depth.
The Takeaway: Think with AI but Don’t Let it Think for You
The true power of generative models isn’t in replacing our thinking, it’s in enhancing it. By engaging in a deliberate, structured process of prompt crafting, we not only get better AI outputs but also improve our own ability to dissect complex problems.
The next time you interact with AI, challenge yourself: Are you setting up a rich conversation with your AI partner, or are you taking the easy way out? Striking the right balance between detail and efficiency ensures that you leverage the full power of these tools without diminishing your own critical thinking.
Happy prompting.