DeepSeek AI Coding Creates Technical Debt. Repository Pattern #aicoding #deepseek #technicaldebt

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.

Видео DeepSeek AI Coding Creates Technical Debt. Repository Pattern #aicoding #deepseek #technicaldebt канала Incomplete Developer