Power of Precision: How AI Reflects Our Words
Imagine taking a photograph, something personal and meaningful, and then using words to guide AI to recreate it so accurately that it becomes indistinguishable from your original work.
Here’s how it works: Start with a photograph you love. Upload it to DALL·E and ask the system to describe it. Don’t stop there—refine the description further. Highlight every detail that matters to you: the softness of the petals, the way light falls across the subject, the arrangement of shapes or textures.
This description becomes your recipe, a set of instructions so precise that the AI can consistently recreate the same visual essence of your photo.
What’s astonishing is that, with enough care, you can go further. By rephrasing and refining, you can forge a prompt that aligns perfectly with the visual truth of your image.
This isn’t just a trick—it’s a revelation about the relationship between words and visuals. It shows that language, when used thoughtfully, can encode reality itself.


The Value of Asking Thoughtfully in AI Interactions
Through the circumstances of my life, I often encounter people’s opinions about AI systems. A recurring theme is their disappointment when the system generates something completely irrelevant to their needs. What I find fascinating is that many times, the issue isn’t with the AI itself but with how it’s being asked to perform a task.
We live in an era where technology enthusiasts have an incredible playground at their disposal—testing, creating, automating, and discovering. However, the disparity in results is striking: sometimes, the system’s output is astonishingly accurate, while at other times, it misses the mark entirely. Why is that?

The Role of Intent and Effort in AI Interactions
My intuition aligns with findings from recent research papers, which suggest that the way we interact with these systems fundamentally impacts the quality of their responses. For example, studies on "Chain of Thought" reasoning have shown that the quality of a prompt can influence the quality of the output by as much as 30%. This finding parallels my personal experiences with AI.
It all ties back to a simple yet profound idea: how much do we value what we ask for? In our upbringing, many of us were taught that if we want something, we should ask nicely. Beneath this simple lesson lies a deeper truth: effort, clarity, and intent often reflect the value we place on our requests.
If this behavior is reflected in the datasets AI systems are trained on—trillions of books, documents, and human interactions—it’s possible that AI can mimic and respond to this effort in kind. When we take the time to structure our requests thoughtfully, the system is more likely to deliver meaningful results.
A Personal Example
Here’s an example from my own experience: I often frame my prompts with context and purpose. For instance, if I’m working on a project that holds professional or personal significance, I make it explicit in the prompt:
"I’m working on a task that’s important to me. People in my environment may encounter this work, and I want it to reflect professionalism. Please help me achieve this goal."
The results often far exceed the generic outputs I’d get with a simpler or vaguer prompt. This aligns with the research, showing how intentional prompts can guide the system to better outputs. It’s not just about asking nicely—it’s about expressing the value and intent behind your request.
The “Strawberry Problem”
Critics often cite humorous examples, like AI’s difficulty in counting the letters in “strawberry.” Even in 2025, large language models can still make this viral mistake. However, if you step back, this is not a failure of the system but a reflection of its probabilistic reasoning. The issue can often be resolved with more targeted or iterative prompting, or by simply asking the system to research its answer.
This highlights an important takeaway: the system’s performance mirrors the effort and clarity we put into our interactions with it.