DeepSeek AI Coding Creates Technical Debt. Repository Pattern

DeepSeek generates a wrong implementation of the Repository Pattern. End up manually fixing a lot of code.

In this video, we explore an important but often overlooked issue when using *AI to generate repetitive code* — the **accumulation of technical debt**.

When you rely heavily on tools like *DeepSeek**, **Claude**, or other AI assistants to automate boilerplate code (especially for patterns like the **Repository Pattern* in software development), it’s easy to overlook small inconsistencies or poor practices that can scale into larger problems.

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🧩 What This Video Covers

We start by discussing a common real-world scenario:
You’re working on a large system with **20 to 50 entities or tables**, and you just want to save time by letting AI generate all the repository classes, data connections, and entity logic.

In this example, we specifically focus on *DeepSeek**, a popular AI model that allows extended usage on its **free tier* — perfect for developers who want to experiment with longer prompts or more extensive code generation without hitting usage limits too quickly (unlike some other models such as **Claude**).

Then, we look at a *repository pattern implementation* involving a few entities — `Inventory`, `Order`, `OrderItem`, and `Product` — and demonstrate how DeepSeek generates the update logic.

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⚠️ Where Things Go Wrong

Here’s where the subtle issues begin to appear:

The generated code *fails to fully populate the entity* (`Inventory`), instead passing each property individually.
*Hardcoded DateTime values* show up in the code — something that works initially, but creates bad habits and maintainability issues.
If you scale this to *dozens or hundreds of entities**, these small issues multiply, creating thousands of lines of low-quality code and **technical debt* that’s difficult to spot or fix later.

This kind of AI-generated shortcut can easily lead to *hidden problems* — logic errors, maintainability issues, and a lack of consistency across your codebase.

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🧠 The Right Approach

Instead of letting the AI hardcode values or create partial updates, the better approach is to:

Use a proper *Update DTO (Data Transfer Object)* that contains all entity properties.
Let your code handle entity mapping in a **structured and consistent way**.
Review and refactor AI-generated code before committing it into production.

AI is a powerful tool for productivity — but without proper review and architecture discipline, it can **generate as much technical debt as it saves time**.

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🚀 Key Takeaways

AI-generated code isn’t always production-ready.
Small shortcuts can lead to **large-scale technical debt**.
Always review, refactor, and understand the code AI gives you.
DeepSeek’s free tier is excellent for experimentation, but not a substitute for good engineering practices.

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🔧 Tools and Topics Mentioned

*DeepSeek AI*
*Repository Pattern*
*DTOs (Data Transfer Objects)*
*C# / .NET Examples*
*Technical Debt in AI-generated Code*

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🏁 Conclusion

AI can be your best coding assistant — or your worst code generator — depending on how you use it.
This video serves as a reminder that even the simplest, most repetitive tasks (like repository generation) can introduce subtle bugs or long-term maintenance headaches if not done correctly.