AI-assisted development allows for faster coding than ever before. However, without understanding the principles of how AI agents work, this speed can easily turn into chaos.
A new project can literally emerge in just a few prompts. But as features are added, bugs fixed, and requirements become more complex, the quality of the code can quickly deteriorate. The AI agent starts making poor architectural decisions, duplicating existing code, and requiring more and more oversight from the developer.
As a result, instead of saving time, you end up spending it checking, correcting, and re-explaining the same tasks.
There is a way to work with AI differently.
This course is not about "vibe coding" or endlessly writing prompts in hopes of a good result. You will understand from scratch how large language models and programming AI agents work, and then learn to build a complete engineering development process with them.
The main goal is to regain control over the code and use AI as a full-fledged developer's tool, not as an unpredictable "magic button."
What You Will Learn
- How large language models work and why it's important for effective interaction with AI agents.
- The structure of programming AI agents, including tools like Claude Code, Codex, and other solutions.
- Context window — what goes into it, why it fills up, and how it affects the agent's performance quality.
- Smart zone and dumb zone — how to determine when the agent is still reasoning well and when its work quality starts to decline.
- Context management — how to maintain a compact context and understand its real state rather than acting blindly.
- Compaction and handoff — how to continue work after filling the context and when to use different methods of work transfer between sessions.
- Requirement refinement (grilling) — how to use AI for consistent idea analysis and transform vague tasks into clear requirements before coding.
- Exploration of an existing project — how to help the agent understand the codebase and use existing solutions instead of creating duplicates.
- AGENTS.md — how to set the AI agent rules for working with a specific project and maintain unified standards.
- Skills — how to create reusable instructions and workflows that the agent can apply when needed.
- Progressive disclosure — how to organize instructions so that the agent receives only the necessary information without wasting context.
- Requirement formulation — how to turn the results of discussions with the agent into a clear specification that can be used to implement the task.
- Decomposition of large tasks — how to break down complex development into small stages that can be reliably executed in separate sessions.
- Transferring context between sessions (handoff) — how to keep the project state and continue work without starting over.
- Subagents — how to delegate separate research and tasks to auxiliary agents with a new context, without overloading the main session.
From Prompts to a Full-Fledged Engineering Process
Instead of the "write a prompt and hope for the best" approach, you will build a predictable AI-assisted development process.
You will learn to consistently go from an idea to a finished result:
idea → requirement refinement → codebase exploration → planning → decomposition → implementation → verification → context transfer → next stage.
This will allow you to work not only with small tasks but also with large functions and projects that cannot be effectively implemented with just one prompt.
Meanwhile, you will maintain control over the architecture, requirements, and code quality.
Practice Instead of Theory
The course is centered around practical work. You won't just study concepts but also understand the internal workings of the environment in which the AI agent operates and apply this knowledge to create real functions from start to finish.
The program includes:
- almost 60 lessons;
- 6 sections;
- videos demonstrating on-screen work;
- detailed written materials;
- step-by-step commands and prompts for independent use;
- tests to check material understanding;
- practical development of real end-to-end functions.
Who This Course Is For
This course is suitable for developers already using Claude Code, Codex, and other AI tools but facing unstable result quality, codebase complexity, and the need to constantly monitor the agent.
It is also suitable for those just starting to use AI in development and wanting to immediately establish the correct workflow without going through the typical "vibe coding" mistakes.
This is not a course on how to make AI write as much code as possible.
This is a course about how to use AI for developing quality software based on engineering principles — understanding what is happening, correctly setting tasks, managing context, controlling quality, and breaking down complex work into manageable stages.
The course is completely standalone. It can be intensively completed in about three days or studied for several hours a day over the course of a week at your own pace.
