AI Development Explained in 10 Minutes: A Structured Approach

The biggest mistake many developers make is rushing through the context-setting phase. There's a tendency to not have the patience to provide the AI with the necessary information to truly solve the problem. However, by slowing down and incorporating a couple of key steps, the entire development process can be significantly accelerated. Nobody is born an expert in this; the only way to figure it out is by getting your hands dirty and seeing what works.

This structured approach can prevent getting stuck in endless loops, especially for engineers and product managers trying to break down a project into actionable steps. It's a time-saver that empowers you to build products and even entire startups more efficiently. While an AI can't replace a dedicated product manager or a CTO's deep thinking, it can certainly enable a single person to build a company. This is the way forward.

The Problem with Unstructured "Vibe Coding"

While 2025 is the year of the "vibe coder," you can't always vibe your way to a scalable execution strategy. To ensure you're not just coding but building the right things, it's essential to bring structure to the process using Product Requirement Documents (PRDs), task lists, and some advanced prompting techniques.

The core lesson from extensive coding with AI is the critical importance of context—what you show the AI and what you ask it to do. You learn to pare down your requests to a manageable scope that the AI can effectively handle.

A Structured Workflow for AI-Powered Development

Here’s a process you can follow using an AI-powered editor like Cursor, which is a fork of VS Code. This example involves a simple CRM tool for a yacht club.

When you need to make a change that is larger than a simple, quick fix, you should start by creating a Product Requirement Document (PRD).

Step 1: Generate a Product Requirement Document (PRD)

A PRD is how you describe a feature you want to build. To streamline this, you can use a pre-written rule or prompt that instructs the AI on how to generate a PRD for you.

A key part of the initial prompt is specifying the target audience. For instance:

"This is a PRD that's suitable for a junior developer to understand and implement this feature."

This instruction helps the AI calibrate its response to the right level of detail. As you code more with AIs, you realize they are like brilliant PhD students who sometimes miss simple, obvious connections. Specifying "junior developer" keeps the output grounded and practical.

To use this, you provide the rule file to the AI as context and then give it a simple instruction.

Example Interaction:

1. Provide the PRD generation rule to the AI.
2. Give the instruction: I'd like to add a report that shows me all the boat names of members and how many emails they've been sent.
3. The AI will ask clarifying questions: The AI, following the rule, will come back with questions to refine the PRD. To make this manageable, the rule should instruct the AI to use dot notation (e.g., 2.1, 2.2) for questions, preventing it from bundling multiple queries into a single bullet point.

*   **AI Question:** "What is the problem this report is trying to solve?"
*   **Your Answer:** "We're trying to give visibility into how many emails people are getting."
*   **AI Question:** "Who specifically will be using the report?"
*   **Your Answer:** "Admins."
*   **AI Question:** "Where should this report be accessible?"
*   **Your Answer:** "You pick."

For the remaining questions, you can often instruct the AI to "make your best judgment." The AI will then generate a complete PRD markdown file, including sections for functional requirements, non-goals, design considerations, etc.

The goal here is to make it crystal clear to the large language model what you want to be done.

Step 2: Generate a Detailed Task List

Once the PRD is created, the next step is to generate a detailed, step-by-step task list based on it. This is done using another rule file, generate-tasks.

Example Interaction:

1. Provide the generate-tasks rule as context. This rule guides the AI on how to create a detailed task list, including the desired format (e.g., markdown with checkboxes).
2. Give the instruction, referencing the PRD: Please generate tasks for [tag the PRD file]

The AI will first propose a set of high-level tasks and ask for approval before generating the detailed subtasks. This structured process, with clear decision points and user interaction moments, brings agentic thinking into a linear, step-by-step chat mode.

The resulting task list will be a well-organized markdown file with epics, tasks, and even sub-subtasks, ready for execution. This step alone is a massive time-saver, as it effectively breaks down a high-level requirement into an actionable engineering plan.

Step 3: Execute the Task List

With a detailed task list in hand, you can begin implementation. A third rule file, task-list-management, provides guidelines for iterating through the tasks.

Key Instructions in the Rule:

• One subtask at a time: This prevents the AI from attempting to tackle too much at once.
• Mark as complete: Immediately after finishing a subtask, the AI should mark it as complete.
• Stop and wait: The AI must stop after each subtask and wait for the user's go-ahead before proceeding.

Example Interaction:

1. Provide the task-list-management rule as context.
2. Tag the task list file.
3. Give the instruction: Let's start.

The AI will begin with the first subtask, thinking through the process, making the necessary code changes, and then checking off the task. It will then wait for you to say "yes" or "proceed" before moving to the next item.

This human-in-the-loop process is crucial. After each step, you can check the AI's work, as it can sometimes introduce small problems or linter errors that need fixing. For version control, you can commit after each major parent task is completed and the application is in a stable state.

Following this process allows you to build huge features—involving thousands of lines of code—reliably and with minimal trouble.

Advanced Techniques for More Control

For more complex scenarios, you can enhance your workflow with a couple of advanced tools.

Using MCPs (Multi-modal Co-pilots)

You can give your AI editor the ability to interact with other applications using MCPs.

• Postgres MCP: This is incredibly useful for database interactions. You can simply ask the AI, "Can you go see if this data is actually in the database?" without writing any SQL. It reduces the toil of switching between multiple tools and interfaces.
• BrowserBase MCP: This allows you to control a headless browser in the cloud directly from your editor. You can instruct the AI to "navigate to the pricing page and take a screen grab." This unlocks a huge amount of potential for front-end testing and automating browser-based tasks.

Mastering Context with Repo Prompt

While AI tools have some magic happening in the background to manage context, sometimes you need explicit control. A tool like Repo Prompt is excellent for this.

It allows you to select specific files and folders from your repository to build a precise context for your prompt. The tool calculates the token count, so you can ensure your context fits within the model's limits.

You can then craft a prompt, such as "How can I improve the maintainability of this code?", and combine it with the selected file contents. The tool wraps everything in clear XML tags, like <file_contents> and <user_instructions>, making it unambiguous for the AI.

This is particularly powerful for heavy-lifting tasks where you need the AI to think deeply about a specific, well-defined context.

Final Thoughts

The key takeaway is that structure and context are paramount. Start small, get comfortable with a simple, repeatable process, and then graduate to more advanced techniques. If you find the AI isn't performing as expected, the solution is often to be more thoughtful. Simply asking it to "please think harder about this, I know you can do this" can sometimes be enough to get it back on track. By treating the AI as a capable partner and providing it with the clarity it needs, you can dramatically improve your development workflow.