Dibe Coding: The Developer-First Approach to AI-native Development

The rise of AI in software development has sparked discussions, excitement and criticism about vibe coding — the idea that developers let AI generate entire codebases based on natural language and without any human code inspection. While the concept is intriguing, it also has some significant downsides. For many professional developers, at least currently , vibe coding isn’t a viable approach: they typically work on large, existing codebases rather than small or medium greenfield projects. They are also accountable for the quality, maintainability, and long-term impact of their code—making blind trust in AI-generated output a risky proposition. As a result, some might assume that if vibe coding doesn’t work for them and that AI has little more to offer than improved autocomplete or help with boilerplate. This misconception leaves a huge amount of potential in between those two extremes entirely untapped. Rather than diving deeper into vibe coding, this article introduces an alternative that better fits the day-to-day reality of professional development.

Enter Dibe Coding — a refined, more practical approach to AI-assisted development that keeps the developer in the driver’s seat while leveraging AI’s capabilities for efficiency. Unlike vibe coding, which often hands full control to the AI and caters primarily to small greenfield projects or prototyping, Dibe Coding is designed for the real-world workflows of experienced developers. It acknowledges the complexities of maintaining large codebases and the necessity of high-quality, maintainable output. By keeping developers in control and turning AI into a collaborative partner rather than an autonomous agent, Dibe Coding is relevant for a much wider group of developers in practice—especially those in professional and enterprise environments.

The “D” in Dibe stands for:

• Delegate
• Design & Divide
• Describe
• Drive & Decide
• Debug & Refine

These five pillars make this method structured, practical, and effective. We’ll introduce each of them briefly in this article, with dedicated deep dives in future posts. If you’re interested in adopting a developer-first approach to AI-assisted development, follow us on LinkedIn to be notified when the next articles go live.

Before we get to the actual concept of Dibe coding, let’s discuss the prerequisites. The technical enablers for this approach are the same as for vibe coding:

1. A powerful LLM for coding such as Sonnet-3-7, o3-mini or Gemini 2.5 (at the time of writing—this may evolve with newer models).
2. A coding agent that connects your codebase with the LLM. We will use the open and flexible agent Theia Coder for demonstration, which is embedded in the AI-powered Theia IDE.

This series explores how to master AI-driven Dibe Coding to enhance, rather than replace, the way we write software. Before we dive into the actual concept, let’s set up Theia Coder as a working environment!

Getting started with AI-driven Vibe/Dibe Coding with Theia Coder

Theia Coder is the core enabler of Dibe Coding—bridging your codebase with a powerful LLM in a transparent, developer-first way. As an integrated AI coding agent within the AI-powered Theia IDE, it helps developers browse code context, generate and apply structured modifications, and interact with AI using natural language—all without losing control of the development process.

For Dibe Coding, this means you can delegate tasks, describe intentions clearly, and review AI-generated changes directly inside your IDE. Whether you’re working on small refactorings or complex features, Theia Coder helps you move faster while maintaining quality.

Learn more about Theia Coder: 🔗 Introducing Theia Coder - the open AI coding agent with full control Browse the Theia Coder Documentation:: 🔗 Theia Coder Documentation Download and install the AI-powered Theia IDE: 🔗 Get the AI-Powered Theia IDE

🎥 Check out the video below to see how easy it is to get started with Theia Coder. We’ll walk through a simple task and show how it fits seamlessly into your AI-assisted workflow.

The Five Pillars of Dibe Coding

“Dibe Coding” or more generically, using AI to generate code efficiently and with full control is a new skill that must be explicitly learned—it’s not just about using AI but about collaborating effectively with it. Just as working with human teammates requires communication, leadership, and structured delegation, working with AI involves similar best practices–with a few differences to working with humans, though. Not every developer is naturally adept at this approach, just as not every team member excels at management. However, mastering Dibe Coding is worth the effort: the productivity gains are undeniable.

One of the biggest hurdles to adoption is acceptance—developers must shift their mindset from seeing AI as a mere autocomplete tool to a true assistant that enhances, but does not replace, their expertise. This shift also involves a transformation in the developer’s role: from being the sole author of code to becoming a strategist, orchestrator, and quality controller in a human-AI collaboration. Instead of focusing solely on implementation, developers now guide the AI, define structure and context, and evaluate the results—much like a team lead working with their fellow team member developers. This new role emphasizes high-level thinking, decision-making, and critical assessment over manual execution.

A Shift Bigger Than Moving From Assembler to Python

Some liken the transition to AI-native development to the historic shift from low-level languages like Assembler to higher-level ones like Python, Java or JavaScript. In both cases, developers had to adjust to a new paradigm and accept a tradeoff: giving up some direct control in exchange for massive productivity gains. But the shift to AI-assisted development—especially Dibe Coding—is even more profound.

When switching from Assembler to Python, the fundamental skill of writing code remained central. Developers still manually crafted logic, just with less syntactic friction. In contrast, Dibe Coding alters not just how code is written, but how developers think, plan, and collaborate. It’s not about writing each line yourself, but about orchestrating the outcome. It demands a different mindset: one where the developer moves from implementer to strategist, using prompts instead of keystrokes to shape logic, structure, and flow.

That said, existing coding skills remain essential. Understanding code and the impact of design decisions deeply, directing architecture, and spotting flawed logic are still non-negotiable. Dibe Coding doesn’t replace these skills—it reframes how they’re applied. Just like a senior engineer leads rather than micromanages every line, developers using Dibe must still lead with insight, experience, and intent.

Fortunately, by diving into the five pillars of Dibe Coding, developers can build this skill iteratively. This allows them to gradually develop confidence in AI-assisted workflows while maintaining control and ensuring high-quality results.

1. Delegate: Deciding What to Offload and What to Keep

One of the biggest challenges in AI-assisted coding is knowing what to delegate and what to handle manually. In practice, developers often fall into two traps: either underestimating AI and doing everything themselves, or overestimating its capabilities and expecting flawless results from poorly chosen and underspecified tasks. Some tasks are a natural fit for AI, while others are better served with traditional tools. If developers choose the wrong tasks, it leads either to frustration—when AI fails—or wasted potential—when AI is underutilized. In a follow-up article, we will explore the nuances of effective delegation, helping developers strike the right balance. See the list of follow-up articles at the end of this page

2. Design & Divide: Plan Before Prompting

A key challenge in AI-assisted development is the need for structured planning before even writing a single prompt. Unlike traditional coding, where developers can make decisions on the fly, Dibe Coding requires thinking ahead more rigorously. Breaking a project into well-defined components, outlining dependencies, and establishing a clear architecture before engaging AI is essential. AI might not naturally infer intent the way human colleagues do and usually has less context information, so without a structured approach, outputs may be inconsistent or misaligned with project goals. The good news is that AI can definitely assist you in preparing a task, too.

Mastering this step ensures AI assistance remains an asset rather than a liability. In a follow-up article, we will explore best practices for planning AI-assisted projects, including how to structure tasks and prompts effectively for complex codebases and how to let AI help you in doing so.

3. Describe: Communicate with Precision

It is not a secret that AI responds best to clear, structured prompts. A vague request leads to unpredictable results, while precise instructions guide AI to generate useful, high-quality code.

Providing good context and the right level of precise requirements is crucial. When you know important details, you should include them in your prompt—just as you would when giving instructions to a coworker, but remember a coworker usually has much more general context than the AI, such as at least a basic understanding of the project goals or its history. A colleague might also ask follow-up questions if something is unclear, but AI often operates differently: it will attempt to solve the problem regardless of underspecified requests, sometimes producing incomplete or erroneous solutions, or even hallucinating entirely. Developers must take responsibility for ensuring that AI has the right information upfront, reducing unnecessary iterations and improving overall efficiency.

4. Drive & Decide: Staying in Control

In most projects, AI-generated suggestions should never be accepted blindly. Developers must evaluate outputs critically, refine them when necessary, and ensure alignment with project goals. Decision-making is an active, continuous process that ensures AI remains an assistant rather than a replacement.

However, the level of scrutiny should depend on the phase and impact of the change. For example, during early development or for local, low-risk changes with strong test coverage, it’s often reasonable to defer detailed review until the overall functionality is complete. This can help maintain momentum and avoid premature optimization. That said, developers should always plan to fully review AI-generated code before it becomes part of the final product—especially when changes affect shared logic, external interfaces, or architectural design.

Striking the right balance—knowing when to review immediately versus when to defer—is key to maintaining both speed and quality. We will dive deeper into this topic in a follow-up article, covering strategies for effective AI-assisted code review and decision-making.

5. Debug & Refine: Test, Fix, Improve, and Iterate

One of the biggest shifts developers notice when adopting AI-assisted coding is that they spend more time testing and debugging than they might be used to. This can sometimes lead to a feeling of inefficiency and frustration—at first glance, it might seem like AI is producing unsatisfactory  results. While flawed task structuring and prompting may contribute to that (see Section 2 and 3 above), there’s another key reason for this perception: AI significantly speeds up code generation, making debugging feel disproportionately longer in comparison. This is also aggravated by the fact that debugging code that you wrote just 5 minutes ago is easier than understanding and debugging the code that the AI generated.

However, rather than seeing this as a drawback, developers can turn this challenge into an opportunity. AI can assist in debugging just as much as in writing code—by generating and executing test cases, explaining errors, and even suggesting fixes. But to make this work effectively, developers need to employ specific techniques that integrate AI into the debugging process efficiently.

By keeping AI in the loop even during debugging, developers can maintain the speed advantages of AI-assisted coding without sacrificing quality. In an upcoming article, we’ll explore advanced debugging strategies for AI-generated code, including how to prompt AI for better test coverage, interpret AI-generated bug reports, and putting the proper code structure in place to enable the AI to iterate efficiently.

Conclusion Turning Vibe into a Viable Workflow

Dibe Coding builds on the raw energy of vibe coding and transforms it into a structured, developer-first approach that’s suitable for professional software development. By introducing clear steps—Delegate, Design & Divide, Describe, Drive & Decide, and Debug & Refine—this method empowers developers to collaborate meaningfully with AI while maintaining full control over the process and results.

At the core of this workflow is a state-of-the-art LLM and a transparent coding assistant such as Theia Coder, embedded in the AI-powered Theia IDE. It bridges your codebase and the LLM, enabling the kind of smart, responsive AI assistance that Dibe Coding relies on.

Just like becoming a team lead, Dibe Coding is a skill that requires upfront investment. It takes time, practice, and intention to master. You can start small, experiment gradually, and build up your confidence and fluency over time—but don’t expect to master it from day one. With consistency, your productivity will increase significantly, but you’ll first need to spend some time getting familiar with the workflow and developing your own best practices.

This article marks just the beginning. In the upcoming follow-up posts, we’ll explore each pillar in depth and provide actionable guidance on how to integrate Dibe Coding into your daily work. See the list of follow-up articles at the end of this page. Also, follow us on LinkedIn to stay in the loop—and learn how to turn AI-assisted development into a repeatable, reliable, and rewarding practice – on your own terms.

🚀 Ready to try it yourself? Download the AI-powered Theia IDE and start coding with Theia Coder today!

If you are interested in building custom AI-powered Tools, EclipseSource provides consulting and implementation services backed by our extensive experience with successful AI tool projects. We also specialize in web- and cloud-based tools and support for popular platforms like Eclipse Theia and VS Code.

👉 Get in touch with us to learn more about how we can help you build custom AI-powered tools.

Follow-up Articles

👉 Delegating Code Tasks to AI: From Challenge to Breakthrough to learn more about how we can help you build custom AI-powered tools.