To get superior results from AI coding agents, treat them like human developers by providing a detailed plan. Creating a Product Requirements Document (PRD) upfront leads to a more focused and accurate MVP, saving significant time on debugging and revisions later on.

To discover high-value AI use cases, reframe the problem. Instead of thinking about features, ask, "If my user had a human assistant for this workflow, what tasks would they delegate?" This simple question uncovers powerful opportunities where agents can perform valuable jobs, shifting focus from technology to user value.

Since AI can "one-shot" a feature once it's clearly defined, the designer's core value is moving upstream. It's no longer about execution, but about navigating the ambiguity of problem framing, opportunity discovery, and stakeholder communication *before* the building starts.

Don't ask an AI agent to build an entire product at once. Structure your plan as a series of features. For each step, have the AI build the feature, then immediately write a test for it. The AI should only proceed to the next feature once the current one passes its test.

Instead of prompting for code line-by-line, "Plan Mode" has the AI agent generate a detailed plan in a markdown file first. The user reviews and modifies this plan like a spec document, elevating their role from coder to architect before the AI executes the build.

AI coding agents compress product development by turning specs directly into code. This transforms the PM's role from a translator between customers and engineers into a "shaper of intent." The key skill becomes defining a problem so clearly that an agent can execute it, making the spec itself the prototype.

Successfully building with AI, even using no-code tools, demands a new level of detail from product managers. One must go deeper than a standard PRD and translate a high-level vision into extremely literal, step-by-step instructions, as the AI system cannot infer intent or fill in logical gaps.

A powerful but unintuitive AI development pattern is to give a model a vague goal and let it attempt a full implementation. This "throwaway" draft, with its mistakes and unexpected choices, provides crucial insights for writing a much more accurate plan for the final version.

AI can get hyper-focused on a specific task and lose sight of the overall user flow. A dedicated "Spec Flow Analyzer" agent can simulate a user persona and review the entire plan, ensuring all necessary steps are connected and the feature is cohesive from a user's perspective.