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 maximize an AI coder's effectiveness, provide it with the same foundational elements you'd give a new human employee: a dedicated workspace (repo), memory (context files), a brief (plan mode), and a clear assignment (ticket). This reframes the AI from a simple tool to an integrated team member.

Vague commands like "improve the design" yield poor AI-generated results. Instead, use intentional, constraint-based language. Words such as "subtle," "refine," and "consistent" act as guardrails, prompting the agent to produce more cohesive and professional outputs rather than making broad, unpredictable changes.

Instead of immediately asking an AI to perform a complex task, first prompt it to create a functional spec or a sequential plan. Go back and forth to align on this plan before instructing it to execute, which significantly improves the final output's quality and relevance.

When an AI agent was given one large prompt to create a design, it ignored parts of the style guide. Gabor theorizes this is due to 'context compression' where details are lost in a large prompt. The solution is to break tasks into smaller, ticketed items, mirroring human workflows to ensure fidelity.

The quality of AI-generated content, whether code or creative design, mirrors the quality of the prompt. Writing a prompt like a detailed product requirements document (PRD), specifying all parameters and definitions of success, ensures the AI delivers the desired outcome, just as it would for a human collaborator.

The best prompts strike a balance between providing enough specific information (e.g., "include code excerpts") and not over-constraining the model. Adding a phrase like "whatever is needed to give me maximum context" gives the AI an "out" to use its own judgment and provide additional, helpful information you didn't ask for.

Using vague, high-level prompts like 'build me a feature that looks like X' is an ineffective 'vibe coding' approach for production codebases. It fails because it doesn't specify *how* the code should work from an engineering standpoint, leading to wasted time, circular iterations, and ultimately unusable output.

Instead of perfecting a single prompt, treat AI interaction as a rapid, iterative cycle. View the first output as a draft. Like managing an employee, provide feedback and refine the result over several short cycles to achieve a superior outcome, which is more effective than front-loading all effort.