Why AI Coding Fails - and How to Fix It

Many developers and teams are experimenting with AI coding — using tools like GitHub Copilot, Cursor, and other AI code assistants — but few manage to make it work reliably in real projects.

At EclipseSource, we’ve spent the past two years exploring every AI coding tool, prompting technique, and context-engineering strategy. What we discovered is that the missing piece isn’t another model or plugin — it’s a workflow.

In our new video, we explain why AI coding often fails and how our Dibe Coding methodology solves the problem. You’ll learn how to apply AI coding effectively, avoid the most common mistakes, and establish a repeatable, reliable process for integrating AI into your daily software development work.

🎓 Master AI Coding with Dibe Coding Training

The Dibe Coding Online Training teaches a complete, tool-agnostic methodology for using AI in software development — whether you’re working with GitHub Copilot, Cursor, or any other AI code generation tool.

You’ll learn:

• How to structure your AI coding workflow for consistent, high-quality results
• When and how to use techniques like context engineering and task engineering
• Practical follow-up actions to efficiently refine AI-generated code
• How teams can collaborate and share common AI workflows

The training is available now and can be taken at your own pace — individually or as a team.

🎥 Watch: Why AI Coding Fails — and How to Fix It

👉 Get the Dibe Coding Training: Dibe Coding Tutorial: AI Coding for Professional Developers

👉 For group or organizational packages, contact [email protected]

📰 Read the full training announcement: AI Coding Training: Dibe Coding Training

ℹ️ More details about the training and methodology: AI Dibe Coding Training

💡 About Dibe Coding

Dibe Coding is a pragmatic workflow for AI-assisted development. It helps developers and teams move beyond “prompting tricks” by introducing a structured, repeatable methodology for working with LLM-based code assistants. The approach focuses on reliability, efficiency, and collaboration — making AI coding truly productive in enterprise environments.

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