#prompt-design

#prompt-design

Claude is a very powerful AI tool that works especially well for coding. It's possible to code entire applications or services in Claude. That's why Claude quickly becomes a very important tool in a product designer's toolkit. It allows us to move quickly and build not only fast interactive prototypes, but also code UI components ready for implementation. To make this guide more specific, I will use Claude to code a sign-up web form.

My role was straightforward: write queries (prompts and tasks) that would train AI agents to engage meaningfully with users. But as a UXer, one question immediately stood out - who are these users? Without a clear understanding of who the agent is interacting with, it's nearly impossible to create realistic queries that reflect how people engage with an agent. That's when I discovered a glitch in the task flow.

AI agents built on large language models (LLMs) often look deceptively simple in demos. A clever prompt and a few tool integrations can produce impressive results, leading newer engineers to believe deployment will be straightforward. In practice, these agents frequently fail in production. Prompts that work in controlled environments break under real-world conditions such as noisy inputs, latency constraints, and user variability. When building AI agents, it may begin hallucinating tool calls, exceed acceptable response times, and rapidly increase API costs.

One of the most persistent problems in AI products today is this: people still aren't sure what these systems are capable of, or how to get the best out of them. This is because most AI tools still greet users with a single blank box and a placeholder as vague as it is open-ended: "Ask anything." The result? Without clear guidance, users start crafting prompts in haste, iterate through endless revisions, lose control of the flow, and gradually pollute the context with fragmented instructions.

What if the secret to unlocking AI's full potential wasn't in the technology itself but in how we use it? After spending over 200 hours teaching AI to write, Nate B Jones discovered that the biggest mistakes aren't about algorithms or software limitations, they're about human misunderstanding. Too often, we assume AI can read between the lines of vague instructions or magically produce brilliance without guidance. The result? Generic, uninspired content that misses the mark.