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.

Even without technical skills, you can develop custom applications by treating your AI coding agent as a dedicated developer. Frame the project with a strong sense of mission and purpose. Persistently push back when the agent says something is impossible. This approach transforms the interaction from a simple command-and-response to a collaborative, goal-oriented development process.

You don't need technical skills to build custom AI tools. Frame your needs as problem statements to a capable AI agent. The AI then acts as a product manager, asking clarifying questions to understand the requirements before generating the necessary scripts and workflows to solve your problem automatically.

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.

Instead of relying on traditional tutorials, non-technical individuals can successfully build complex AI agent teams by using a conversational AI as an interactive, patient, step-by-step coach. This approach democratizes access to advanced technology, bypassing conventional learning methods.

Ramp's internal tool, "Inspect," allows non-technical roles like PMs and designers to generate and merge production-ready code. This dramatically accelerates development for quality-of-life improvements and minor features, activating the entire company as builders, not just the engineering team.

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 writing code, engineers verbally describe a feature, use an AI to generate a detailed spec, and then point another AI agent at the spec to build the feature. The spec file becomes the source of truth, managed in version control.

Traditionally, building software required deep knowledge of many complex layers and team handoffs. AI agents change this paradigm. A creator can now provide a vague idea and receive a 60-70% complete, working artifact, dramatically shortening the iteration cycle from months to minutes and bypassing initial complexities.