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.
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Even though modern AI coding assistants can handle complex, single-shot requests, it's more reliable to build an application in stages. First, build the core functionality, then add secondary features, and finally add tertiary elements like download buttons. This iterative approach prevents the AI from getting confused.
For AI products, the quality of the model's response is paramount. Before building a full feature (MVP), first validate that you can achieve a 'Minimum Viable Output' (MVO). If the core AI output isn't reliable and desirable, don't waste time productizing the feature around it.
Instead of codebases becoming harder to manage over time, use an AI agent to create a "compounding engineering" system. Codify learnings from each feature build—successful plans, bug fixes, tests—back into the agent's prompts and tools, making future development faster and easier.
For large tasks like creating a business plan, act as a project conductor. First, prompt the AI for individual components like a table of contents or specific sections. Once all parts are generated, use a final prompt to synthesize them into a coherent whole.
Achieve higher-quality results by using an AI to first generate an outline or plan. Then, refine that plan with follow-up prompts before asking for the final execution. This course-corrects early and avoids wasted time on flawed one-shot outputs, ultimately saving time.
To tackle complex projects like designing a new partner program, a structured, conversational approach with AI is highly effective. By prompting with "Let's take it one question, step at a time," users can turn the AI into a collaborative strategic planner.
The most effective way to build a powerful automation prompt is to interview a human expert, document their step-by-step process and decision criteria, and translate that knowledge directly into the AI's instructions. Don't invent; document and translate.
The most leveraged engineering activity is creating a 'meta-prompt' that takes a simple feature request and automatically generates a detailed technical specification. This spec then serves as a high-quality prompt for an AI coding agent, making all future development faster.
The panel suggests a best practice for AI prototyping tools: focus on pinpointed interactions or small, specific user flows. Once a prototype grows to encompass the entire product, it's more efficient to move directly into the codebase, as you're past the point of exploration.
Instead of immediately building, engage AI in a Socratic dialogue. Set rules like "ask one question at a time" and "probe assumptions." This structured conversation clarifies the problem and user scenarios, essentially replacing initial team brainstorming sessions and creating a better final prompt for prototyping tools.
