AI Coding at Scale: Structure Your Workflow with Dibe Coding

AI-powered development is everywhere. From YouTube tutorials to conference talks, from open-source demos to enterprise prototypes - coding with AI is the new frontier. One-shot prompts that generate entire applications. Agents that debug your code. MCP servers that enhance context awareness. The examples are fascinating and often impressive.

Yet when it comes to applying AI coding in an enterprise setting, things get complicated.

Most companies follow a “crowd learning” approach: give developers access to a tool, maybe even let them choose the one they prefer, and then hope they figure out how to use it well. And some do - a few developers boost their productivity, invent clever workflows, and enjoy the magic. But many don’t. Some feel overwhelmed. Some even slow down significantly. Recent studies confirm what we’ve seen in practice: unguided and unstructured AI tool adoption leads to very mixed outcomes.

Through real-world experience, we’ve discovered that successful AI-native development in teams requires a few critical building blocks. One of them is process. Not rigid procedures or heavyweight rules - but a shared, simple structure that guides developers as they collaborate with AI.

That’s where Dibe Coding comes in.

Dibe Coding defines a lightweight, structured process for working with AI coding agents. It doesn’t rely on specific tools. It doesn’t dictate what developers must do. Instead, it makes the implicit explicit - turning messy trial-and-error into a clean and discussable workflow. It enables developers to share techniques, adopt best practices, and talk about their approach in a structured way. And developers love structure.

A full description of the Dibe Coding process - and especially the best practices for each step - goes well beyond the scope of this article. This piece is meant as an overview: it introduces the terminology, outlines the process, and sets the stage. For several steps, we’ve already published dedicated articles and videos, and we’ll continue to share more. Follow us LinkedIn and YouTube to stay up to date with the latest content.

So without further ado, let’s look at the simple but powerful process behind Dibe Coding.

An overview on the process of Dibe Coding

Step 1: Decide – Is the Task Suitable for AI?

Every Dibe Coding session starts with a key question: should this task be delegated to AI?

If you delegate the wrong tasks to AI, you’ll likely get frustrated early. And if you don’t delegate the right ones, you’ll miss key opportunities to speed up or improve your work. This step in the process matters because it makes the decision explicit - giving you a chance to reflect, learn, and improve your judgment over time.

🔗 Want the deep dive? → Read the full article on delegation AI tasks in Dibe Coding

💡 Two quick hints to get started:

1. Try to delegate every task you reasonably can - even if it feels slower at first. You’re building fluency, not just saving time.
2. Avoid delegating static or automatable tasks - like repetitive refactorings or simple transformations. Use traditional tools or have the AI help you build automation instead.

Step 2: Define – Shape the Task and Provide the Right Context

Once you’ve decided to delegate a task to AI, the next step is to “define” the task before it is handed over to an AI - and this step makes or breaks your success.

It’s no coincidence that most of the innovation and best practices in AI coding today are concentrated in this phase. Techniques like context engineering, retrieval-augmented generation (RAG), persona modeling, vector-based memory, and structured prompting have emerged precisely because defining the task well is the strongest lever for improving outcomes.

Define in Dibe Coding is a two-part process:

1. Task Engineering – shaping the task so it’s clear, feasible, and AI-friendly
2. Context Engineering – assembling the information the AI needs to solve it effectively

These two activities are tightly interwoven. As you dive into the task and start crafting the context, you’ll often refine the scope, restructure the goals, or break the task down further. This happens because the act of detailing a task forces decisions-you start noticing what’s unclear, identify gaps in your understanding, and uncover hidden complexity. In other words, the process of defining reveals the true shape of the work.

Task Engineering: Shape Before You Prompt

This is where you plan the task-not in exhaustive detail, but with enough clarity to give the AI a meaningful starting point and a clear expected result. It often involves:

• Splitting the task into subtasks (“Divide”)
• Refining vague requirements
• Outlining the intended architecture or desired result (“Design”)

You don’t need to overthink this-but you do need to think the task through, anticipate what could be misunderstood, which aspects are underdefined and externalize implicit expectations surrounding your project. Remember, time invested in this step saves a lot of time reviewing the “wrong” solution later on.

Not every prompt needs to be atomic, but tasks should be coherent and scoped enough for enabling an efficient review and validation. This isn’t about waterfall design-it’s lightweight forethought, balancing structure with flexibility. For more on dividing tasks effectively, we will soon publish a dedicated article so follow us on LinkedIn to get notified.

Context Engineering: Feed the AI What It Needs

Even a perfectly shaped task can fail if the AI lacks the right context. It’s worth reiterating: this is the hottest area in AI-native development today. Prompt engineering has evolved into context engineering for a reason-this is where teams are investing most heavily, from prompt frameworks and memory managers to custom context packagers. In this article, we’ll only scratch the surface.

Good prompting means good context, typically in three categories:

Task Context

A detailed description of what the AI should do. Include:

• Clear goals and instructions
• Related code or files
• Examples, if helpful

✳️ Consider formalizing this as an artifact rather than just using chat. Structured task context improves reproducibility and team collaboration. See the video below on how to treat task context as a reusable unit.

Project Context

Information the AI needs about the wider system, like:

• Architecture overviews
• Coding conventions
• File structure or module responsibilities

✳️ Again, structured artifacts can help. Rather than improvising each time, consider maintaining a shared project context like shown in the video below.

System Message

Persistent, high-level instructions that guide the agent’s behavior.

Some tools keep this system prompt hidden or static-you may not know what instructions the AI is using under the hood. Others let you choose between predefined modes, such as “edit” vs. “agent” mode, which tweak the system prompt behavior. More advanced platforms, like the AI-powered Theia IDE, offer full transparency: you can view, modify, and even version-control the system prompt itself. This level of control can significantly improve consistency and adaptability across AI-assisted development sessions. In any case, you should be aware that the system prompt is part of the context you provide to the underlying LLM.

Together, these layers give the AI enough signal to produce output that fits your world, not just its training data-and using structured artifacts makes that signal clearer and more repeatable.

💡 Three Hints for Better Task Definition

• Invest in Preparation Spend more time preparing the task. It might feel slower upfront, but it pays off by reducing review cycles and debugging time later. Clear definition leads to better AI output.

• Externalize Your Artifacts Don’t leave your task context scattered in a chat conversation. Use external files (e.g. markdown or JSON) to structure your input. Think of context as your main input into the system-make it reusable, inspectable, and shareable.

• Let AI Help You Define
  Use AI not just to solve tasks, but to prepare them. Prompt it to generate task contexts, gather relevant context for that task, then iterate together. This not only saves time but also sharpens your thinking.

Step 3: Invoke – Prompt the AI with Context Artifacts

With your task and context ready, it’s time to prompt the AI-but in Dibe Coding, prompting means more than typing in a clever message.

Instead of ad hoc chat, you initiate the session with the structured artifacts you prepared earlier-like markdown files or reusable context bundles. This makes your process more reproducible, inspectable, and consistent across team members and sessions.

Modern tools like the Theia IDE make this seamless. You can launch AI sessions with attached files or selected code regions-no need for messy copy-pasting. And even in simpler environments, referencing well-structured artifacts keeps the workflow clean and ensures the AI gets exactly what it needs.

The goal: make the handoff to AI predictable and repeatable. The better the prep, the more mechanical and reliable the actual prompting becomes.

Step 4: Await – What to Do While the Agent Works

Once you’ve prompted the AI, there’s a moment of pause. The agent is thinking, generating, synthesizing-doing its job. But what about you?

This moment might feel like a productivity dead zone. And yet, how you handle this “idle” phase can have a surprising impact on your overall efficiency. It’s a deceptively tricky part of the workflow.

The Idle Paradox

At first glance, it might seem like a good time to multitask. But there are two traps:

1. Unpredictable Wait Time – You don’t know how long the AI will take. It might return in seconds-or minutes. That makes it hard to commit to anything substantial.

2. Context Switching Is Expensive – Switching to an unrelated task can drain your cognitive energy. When the agent’s response arrives, you might not be mentally prepared to engage with it-especially if you were deep into something else.

Even with fully autonomous agents that chain together multiple steps, this problem doesn’t disappear. The review/decide step still demands your full attention, and mental detours into unrelated tasks can lead to longer ramp-up times and reduced review quality. Studies show that shifting to a completely different context-even briefly-can leave lingering cognitive residue that hampers focus.

So, what should you do?

💡 Three Productive Strategies for the Idle Phase

Here are some good practices for staying in flow without draining your brain:

• Stay in the Context
  Use the time to prepare for what’s next. Think about follow-up prompts. Sketch out the next related task. Jot down questions you’ll want to ask. This keeps your mental state aligned with the AI’s output.
  👀 Consider actively observing the AI’s behavior during the first couple of minutes. Early signs can tell you whether the agent is heading in the right direction-or missing the mark. It’s often worth pausing to reflect and, if needed, cancel the run to add missing details or correct the course before it drifts too far. This kind of lightweight early supervision can significantly improve both speed and quality.

• Do Micro Work If you must divert, choose something lightweight and contained-like checking notifications, clearing a couple of emails, or updating a to-do list. Avoid deep-focus tasks.

• Take a Short Break
  Sometimes the best move is no move at all. A stretch, a breath, or a moment away from the screen can recharge you. That way, you return with full attention when it’s time to review and steer the session forward.

Remember, you’re not waiting-you’re preparing. Use the idle time to support the next step, not distract from it.

Step 5: Review & Decide – The Core of Dibe Coding

This is where it all comes together.

This is also the moment that many YouTube tutorials tend to skip with a cut scene. You’ll often hear a line like, “The output was almost perfect - just needed a little tweaking.” But what does that actually mean? What kind of tweaks? What counts as “almost”?

In practice, this step involves close inspection of the AI’s output: checking whether it aligns with your intent, follows architectural patterns, contains any bugs, and integrates properly with the surrounding codebase. The level of review can vary depending on the phase of work. At first, a quick glance at the suggested diff is enough; later, a full end-to-end test is warranted. Let the context and confidence level guide the depth of your review.

The point is: this step demands critical thinking. It’s not just about minor edits - it’s about evaluating whether the AI moved you forward in the right direction, and deciding how best to continue. Remember, eventually you are responsible for the code you generated with AI, you need to take full ownership whether it’s glory or failures.

This review & decide step is arguably the most critical in the entire Dibe Coding loop-second only to preparation.

Why? Because this is the moment of judgment.

The Art of Judging AI Output

You’re not just reviewing code-you’re evaluating whether the agent understood your intent, applied the right structure, followed conventions, and moved you closer to your goal. It’s part quality check, part strategic reflection.

The crucial questions are: How deeply do you review or already test before deciding on the next step? And what should that step be?

There’s no strict rule here, but a good heuristic is this: review until you find the first meaningful issue. Don’t aim for exhaustive perfection up front-let the first failure be your trigger to act. Often, letting the AI try again is cheaper and faster than over-analyzing or editing manually.

Then comes the decision: what’s your next move? Should you refine, redo, divide, or escalate? The next step depends on what you find-but the key is to choose quickly and deliberately. As you gain experience, you’ll develop intuition for the right level of scrutiny and the appropriate follow-up action in each scenario.

The Productivity Pitfall

Here’s the hidden danger: you can easily lose time here. Overanalyzing. Second-guessing. Perfecting before it’s needed. This is where many developers slow down, unsure of how to proceed. And that’s okay-at first.

But with practice, you’ll start building intuition on how much to spend on review in which phase and how to decide quickly on the next step. The faster and more confidently you can make these calls, the more fluid your collaboration becomes-and the more productive your AI-native development workflow will feel.

So don’t aim for perfection. Aim for progress. Review with focus, decide with clarity, and loop forward.

Step 6: Follow-Up Actions – Steering the Session Forward

After reviewing the AI’s output and making a decision, it’s time to act. In Dibe Coding, this means choosing a follow-up action-and often, that means crafting the next prompt. But this round is different from the first.

You now have:

• Generated code as a starting point.
• A session history you can build on.
• Insight into the agent’s direction-what it understood, how it interpreted your instructions, and where it might have gone off track.

This changes the game. You’re not just prompting from scratch; you’re guiding, nudging, correcting, or pivoting based on where the AI is now.

There’s no one right answer. These actions are flexible and often combinable. The art lies in knowing which to apply when-and that comes with practice, not checklists.

🔁 Redo

Scrap the output and try again. This usually means going back to your original context, modifying it (often clarifying, tightening, or restructuring), and starting a fresh generation.
Use this when:

• The result is off in tone, structure, or scope.
• The agent misunderstood your intent fundamentally.
• There is something wrong that is just minor.
• You haven’t reviewed much yet but identified the issue quickly.

💡 Hint: Redo is an underrated and often underused action. But when your original context is well-crafted-meaning it produces reproducible results-a redo can be one of the most efficient options. It lets you regenerate a clean, corrected state without accumulating inconsistencies.

🛠 Refine

Send an additional prompt in context to adjust or improve the output. It’s fast, conversational, and works well if the result is close but needs tweaks. In practice, this usually means writing a follow-up message in your chat session to ask the coding agent to correct or change something - such as renaming a variable, reordering logic, or applying a small fix.
Use this when:

• You want to improve something minor such as naming, structure, or fix a minor misunderstanding.
  You’ve already reviewed large parts of the outcome and don’t want to risk a full regeneration (redo).

💡 Hint: Refine is the most common follow-up action developers reach for - and for good reason: it’s fast and intuitive. But be cautious. Each refine step adds to the session history and context. If the conversation grows too large, performance and clarity may suffer. If a problem persists after several refinements, consider using a redo, splitting the task out or to summarize the session to regain clarity and control.

✂️ Divide or Split Out

This means to separate your work into multiple sessions.

• Divide the task when it’s too large or complex-refactor it into smaller subtasks, and re-run each one.

• Split out a separate issue when you identify a related but distinct task (e.g. a newly discovered bug). Finish your current task and then create a new prompt and context for that.

💡 Hints:

• For Divide: A task is too complex if you cannot describe it clearly enough to the AI. It is also too large if you cannot review and test it in a reasonably fast way. Consider both criteria-and in general, don’t hesitate to work with smaller tasks. While it may feel slower, the faster iterations often lead to more efficient outcomes overall.

• For Split Out: A related task can be something you discover during review or a shortcoming in the current generated solution that is clearly understood. Instead of stacking multiple refinements in one session, consider splitting them into follow-up tasks. The smaller the task, the easier the context engineering and review become.

🧾 Summarize Session

Long chat sessions can become noisy. Summarizing condenses the conversation and establishes a fresh baseline. Use this when you’ve refined multiple times and you have the feeling precision is decreasing. Some tools do this summarization in the background. In the Theia IDE for example, you can trigger this action in a transparent way.

💡 Hint: When in doubt, summarize. It’s like rebooting your brain and the AI’s context-cleans things up, gives you clarity, and helps prevent “chat soup.” Summarize more often than you think you need-it rarely hurts, often helps, and sometimes saves the day.

💾 Create Save Point

Preserve the current code state, e.g. by committing the current state to a branch.
Use this when:

• You’re about to experiment further, e.g. with a redo

• You want to refine and use an agent mode, potentially iterating a lot on the code

💡 Hint: Build the habit of committing your work after every review step and documenting the latest state (including open questions or shortcomings). This adds structure, helps you recover from wrong turns, and protects your progress in case of interruptions. It’s like autosave-but smarter.

⚙️ Adapt Project Context or System Message

When the AI makes a generic mistake-like using the wrong testing framework, missing key coding conventions, or commenting inconsistently-it often points to gaps in your project context or a suboptimal system message.

This isn’t just something to fix when mistakes repeat. You should adapt the shared context or system message immediately when you notice something broadly applicable is off, even if it’s the first occurrence. Small context issues can lead to compounding inefficiencies if left unresolved.

Use this when:

• The AI misses architectural patterns or system-specific constraints.

• It disregards team conventions, such as naming styles or preferred libraries.

• The tone or style of generated code doesn’t match your standards.

💡 Hint: Don’t wait for problems to repeat. If an issue reflects a generic misunderstanding, fix the context or system prompt right away. And better yet, treat this as a team responsibility-encourage a culture where developers regularly update and maintain shared project context and system guidance as part of the collaborative AI workflow. Many tools allow you to add rules for these adaptations, in the Theia IDE, you can use a project info and adapt any agent prompt.

Learn more about managing project context in the video below:

🛑 Two Non-AI Options

Not every situation calls for another round with the agent. Sometimes, you just move on:

🚪 Escape / Manual Code

If the AI can’t get it right or it’s faster to do it yourself, just write the code. That’s not a failure-it’s a choice.
Use this when:

• The change is too subtle or too domain-specific, so you’re confident that manual work is quicker.

💡 Hints:

1. You can still return to AI coding after making manual changes-but the AI must be made aware of what you did. The easiest way is to start with a new context that reflects the updated state. If you continue in the same session, be explicit about your manual edits so the AI can incorporate them. This counts as a refine step, with all its associated considerations.

2. Don’t escape too early. Manual edits often seem quicker, but the more you do by hand, the more you’ll have to explain to the AI later. Many manual edits are signals of missing context-often project-level, not just task-specific. Use this as a cue to improve your artifacts before jumping ship.

✅ Done

Sometimes, it’s just… done. The code works. You’re satisfied. Get a cup of coffee, enjoy, and reflect on how much time you saved.

💡 Hint: Your “done” step can be AI-assisted too. You might use an agent like Theia AI’s App Tester to validate the new feature, or a Git agent to help you commit your work. With externalized task context, this becomes even easier-you can reuse structured descriptions of what was built to verify or document the result.

Conclusion: Structure Is Your Superpower

Dibe Coding isn’t just a fancy process-it’s a framework to help transfer the huge potential of AI to professional enterprise development, dealing with large code bases and complexity, beyond toy projects on Youtube. In a landscape overflowing with powerful tools but lacking consistent practices, structure becomes your superpower. It transforms AI coding from scattered experiments into scalable workflows, making collaboration smoother, outcomes more reliable, and learning faster.

Each step in the Dibe process is designed to help you think clearly, work efficiently, and stay in control. It’s not about restricting creativity. It’s about giving it shape. And once that shape becomes second nature, AI becomes not just a tool you use, but a teammate you lead.

This article laid the foundation. The real depth lies ahead: in the nuances of delegation, the art of context engineering, and the dance of human judgment guiding machine output. We’ll explore these in upcoming articles. If you want to sharpen your skills and master AI-native development, follow us on LinkedIn and YouTube for deep dives, demos, and updates.

💡 If you want to master this method end-to-end, take our online AI “Dibe Coding” Training. Learn more on the training page or book now (50% OFF).

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