Delegating Code Tasks to AI: From Challenge to Breakthrough

Vibe coding—the concept of letting AI generate entire codebases from natural language prompts—offers a glimpse into what’s possible with today’s large language models. But as we’ve discussed, it often falls short in real-world scenarios, especially for professional developers working on complex, long-lived systems. That’s where Dibe Coding comes in: a structured, developer-first alternative that blends human expertise with AI’s generative power. In case you missed our first article introducing the concept of Dibe coding:

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

The first core pillar of Dibe Coding is Delegation—but before we can delegate well, we need to learn how to select the right tasks to be delegated. In fact, task selection is the first and arguably most important step in the entire process. It’s also one of the hardest to learn.

Why? Because state-of-the-art LLMs are surprisingly capable—often far more than we expect. They can tackle intricate refactorings, generate documentation, scaffold entire features, and even help with testing strategies. But this power is double-edged. The sheer variety of what they can do makes it difficult to figure out what they should do. Delegating to AI is, in that sense, just as nuanced as delegating to a coworker—it’s a valuable skill that not everyone finds intuitive or enjoyable at first. Like human delegation, it requires trust, communication, and calibration over time.

Selecting the right tasks to delegate isn’t a checklist skill—it’s a soft skill. It involves judgment, intuition, and iteration. It’s contextual. What works in one project might fail in another. What worked last week might break next week as models, tools, and your own workflow evolve. You won’t master this skill by reading a guide—you’ll develop it by trying, failing, refining, and repeating.

This article will give you a starting point: a way to think about task delegation more effectively. But remember—like any real skill, it only clicks once you get your hands dirty. Treat delegation as an exploration. Stay curious. And keep pushing the boundary between what you do yourself and what AI can help you with.

Two Simple Rules to Kickstart Delegation

After all that talk about how tricky delegation is, let’s cut through the noise with two dead-simple rules to get you started:

Rule #1: Try to delegate every single coding task you possibly can to AI.

Rule #2: Accept upfront that it will (at least feel like it) take longer than doing it yourself.

At first glance, these might seem counterintuitive—why hand off everything to AI if it slows you down? But that’s exactly the point: early on, the goal isn’t speed, it’s exploration. Delegating broadly helps you discover what the AI is capable of, where it struggles, and how you can improve your prompts. Accepting the slower pace frees you from frustration and reframes the process as skill-building. Like training a new team member, the more you engage with the AI now, the faster and more effective your collaboration and delegation will become later. You’re not just saving time—you’re learning how to push the boundaries and guide the AI with precision.

This mindset of experimentation—of trying even when you’re unsure—sets the stage for a helpful metaphor that illustrates what it’s like to work with AI in development.

Be Christopher Columbus Every Day: The Art of Crossing with AI

Think of every coding task as a lake you need to cross. You always have the option to walk around it—that’s manual coding. It’s familiar, predictable, and most developers know how to do it well. But next to that lake, there’s also a harbor full of sailboats—each representing a different large language model and AI-powered tool like Theia Coder. These boats can potentially carry you across much faster—but only if you know how to sail.

At first, sailing might feel slower than walking. You might pick the wrong boat, you have to set it up and leave the harbor, you might struggle with steering, or even end up going in circles. But that doesn’t mean the sailing route is inherently worse. The outcome depends on many factors: your skill in choosing which tasks to delegate, how well you design and divide the problem, how clearly you describe it, and the capabilities of both your AI model and your tools.

The good news? With a controlled approach such as Dibe coding and a transparent AI coding assistant like Theia Coder, you’re never locked in. If the sea gets stormy or you encounter a narrow strait, you can always step off the boat and walk a few meters—manually code that tricky bit—and then hop back on the same boat or even switch to a new one. This flexibility is essential: it turns AI from an all-or-nothing gamble into a tool you can fluidly integrate with your workflow.

If your first crossing fails - and it likely will - , it doesn’t mean you should always walk—it just means you’re still learning to sail. And learning to sail is worth it. Once you’re proficient and pick the right boat, you’ll not only cross lakes faster, but you’ll be ready for oceans.

Keep in mind: without being an experienced sailor, it’s impossible to know upfront whether sailing or walking will be faster for a given lake. Complex tasks—those wide lakes with forks and islands—make this even harder to judge. That’s why you need experience, experimentation, and a willingness to embrace uncertainty. People often underestimate how long walking will take, especially if the path turns out to be longer or more winding than expected. And if your target shifts during the journey, it’s far easier to pivot in a fast-moving boat on the lake than on foot along entrenched trails.

And remember: building a new skill requires an upfront investment. In the short term, you may feel slower and less efficient. But this is part of the process. Just like training a new team member, the time you spend now learning to collaborate effectively with AI will pay off with increased productivity and better results later. You’re not just working through tasks—you’re building intuition, skill, and a more powerful development approach.

So be Christopher Columbus every day. Set out across lakes where you have no idea what’s on the other side or how to reach it. You’ll get better at sailing only by sailing. And as your skills grow, so will your ability to cross bigger, more ambitious waters—transforming the way you build software.

Or the TLDR version of this section, is rule number 3:

Rule #3: Push the Boundary: Try a bit more than you think will actually work. This is how you discover the real boundary of what’s possible with your current AI tooling, models, prompts, and experience. It’s not about playing it safe—it’s about nudging the frontier forward, one experiment at a time. Every stretch expands your intuition and improves your ability to navigate the collaboration between human and machine.

But now enough with metaphor—let’s get to some down-to-earth best practices that will help you delegate code tasks more effectively.

Best practises

Describing best practices for selecting delegatable coding tasks is hard—there’s enormous variety in context, tools, models, and developer experience. However, certain patterns do emerge in practice. Below, we’ll outline two types of tasks you should generally avoid delegating directly, and three types you might not think to delegate—but absolutely should. These patterns serve as helpful heuristics as you build your own intuition for effective delegation.

Best Practices: What Not to Delegate to AI

While we recommend delegating broadly and learning through trial and error, some task types consistently make poor candidates for AI assistance. Recognizing these anti-patterns can save time and frustration.

Static Refactorings Many developers new to AI-assisted coding gravitate toward tasks they’re already familiar with from existing tools—like renaming symbols, static autocompletion, organizing imports, or changing a version number across hundreds of files. While it may seem intuitive to hand these off to an LLM, doing so is often inefficient and counterproductive. These tasks are highly deterministic and better suited to traditional tools that offer guaranteed accuracy. AI is not a deterministic machine—it’s more like a helpful coworker. If you already have reliable tools for a task (like refactoring tools built into your IDE), use them. When working in flexible environments like Theia AI, you can even connect deterministic tools—such as Git or language servers—so that your AI assistant can trigger and integrate these precise operations, combining flexibility with reliability. See this example on how to connect tools to an AI in Theia AI:

👉 Let AI commit (to) your work - With Theia AI, Git and MCP

👉 Learn more about Theia AI: The Open Framework for Building AI-native Custom Tools and IDEs

Automatable Static Tasks Another common and similar pitfall is using AI for tasks that should be automated, not manually executed through conversational instructions. If a task is deterministic but no built-in tool exists, don’t delegate the manual execution—delegate the creation of automation. For example, instead of asking the AI to analyze 1,000 log files one by one, ask it to write a script that automates the analysis. This shifts the AI’s role from dull laborer to capable toolsmith. You’re not outsourcing grunt work—you’re accelerating innovation. Once you’ve delegated the creation of such scripts or workflows, you can again take it one step further and integrate them into your AI environment. This enables future delegation of complex tasks that leverage efficient, deterministic automation under the hood.

Best Practises: What to Not-Not Delegate to AI

Underdefined Tasks Counterintuitively, underdefined tasks—like building a new UI component, a new service implementation or prototyping a fresh application—are often great candidates for AI delegation. This is where modern LLMs shine: they excel at filling in the blanks with plausible, high-quality defaults, learned from thousands of examples in the wild. While you may not provide a full spec, the AI can generate surprisingly coherent starting points, helping you explore directions quickly and iterate from there. Instead of stalling on unclear requirements, let the AI give you something tangible to react to and refine.

Overdefined and Simple Tasks On the flip side, don’t hesitate to delegate even very simple or overly clear-cut tasks. It might feel like more effort to describe the task to the AI than to just do it manually—say, improving a UI label. But delegating these tasks still brings value. First, they’re low-risk and likely to be completed flawlessly by the AI. Second, the AI acts as an extra set of eyes—catching similar issues elsewhere, identifying inconsistencies, or proposing small improvements you hadn’t considered. A quick AI-assisted change might fix more than just the one thing you asked for, and it helps you stay in a flow state rather than context-switching into minor edits.

Small Refinements During Existing Tasks A common frustration in AI-assisted development arises when developers manually adjust the output of an AI, only to have those changes overwritten by the next AI-generated suggestion. This back-and-forth leads many to abandon AI tools prematurely. Fortunately, there’s a simple and effective solution: avoid making small refinements manually outside of the AI’s visbility—delegate them to the AI instead.

This strategy ensures the AI “learns” your preferences within the specific context of your current task. For example, imagine your project’s coding guidelines require single quotes, but the AI generates code with double quotes. If you manually change the quotes, the AI may ignore your fix and revert to double quotes later. But if you ask the AI to make the adjustment, it will incorporate that preference into its understanding of the current task—resulting in more consistent output moving forward.

Again, think of the AI like a coworker—you wouldn’t silently clean up their code without mentioning it. Instead, you’d tell them what needs to be changed so they can learn and improve. The same applies here: teaching the AI helps it adapt to your expectations.

To make such preferences persistent across your projects, you can go one step further by adapting the underlying prompts. This is well supported in Theia AI. For instance, the AI-powered Theia IDE lets you easily define global or project-specific instructions for all agents. You can simply instruct Theia Coder to always use single quotes, as demonstrated in the video below. These adaptations ensure your preferences are consistently respected—turning one-off refinements into lasting improvements.

Conclusion: Build Your AI-native Future, Your Way

Delegating coding tasks to AI isn’t just a technical challenge—it’s a new way of thinking about software development. The learning curve can feel steep, but the payoff is worth it. Like any powerful tool, AI delivers the most value when used skillfully, intentionally, and with a clear understanding of its strengths and limits. The journey begins with curiosity, grows through experimentation, and becomes second nature through consistent practice.

The sailing metaphor captures this shift well: at first, walking (manual coding) feels faster and more reliable. But as you learn to sail—by trying, adjusting, and exploring—you’ll discover that AI can take you further, faster, and with less friction. Some crossings may be choppy, but each one builds your capability. The only way to master the wind is to hoist the sail.

Dibe Coding is more than a method—it’s a mindset shift. It turns AI from a flashy novelty into a dependable tool, helping you navigate the complexities of real-world software development with clarity, control, and creativity. By mastering the five pillars—Delegate, Design & Divide, Describe, Drive & Decide, and Debug & Refine—you’re not just speeding up your workflow, you’re transforming the way you think about coding itself.

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

🧭 Up next in this series: “Design & Divide – How to Think Like an Architect Before You Prompt.” This follow-up dives into the second pillar of Dibe Coding, showing you how to plan, structure, and divide tasks effectively before engaging the AI—one of the most overlooked but impactful steps in AI-assisted development.

👉 Follow us on LinkenIn or Twitter to be the first to know when it drops—and to keep learning how to level up your AI collaboration skills.

But why stop at general-purpose tools? The real magic happens when AI tooling is shaped around your exact needs. At EclipseSource, we specialize in building custom AI-native tools and IDEs tailored to your domain-specific workflows—whether you’re working in software engineering, embedded systems, hardware development, construction, or beyond. Our team brings deep technical expertise and years of experience in tool creation to help you design intelligent development environments that fit your world perfectly.

🚀 Ready to explore what’s possible? Get in touch—and let’s build your next-generation AI-powered tooling, together.

Appendix: 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.