From Model to Patch: Building a Legacy Code Auto-Repair Assistant

As an application architect deeply involved in the AI field, I often receive inquiries from independent developers and small technical teams: "I want to add an AI feature to an existing IDE or editor plugin, what's the fastest way to land it?" or "There are so many LLMs on the market, which one should I integrate?"

Actually, for developers looking to implement AI applications quickly, the core challenge is often not the principles of the model itself, but the last mile of engineering: how to transform the model's reasoning capabilities into specific business value, such as generating a directly applicable code patch.

Today, using a typical scenario—the "Legacy Code Auto-Repair Assistant"—as an example, we will guide you through the entire process from architecture design to code implementation, covering everything from model integration to patch generation.

1. Business Pain Points: Why "Chat" Does Not Equal "Productivity"?

Many developers usually stop at the "Chat Bot" stage when trying to integrate AI. The user asks: "Is there a bug in this code?" The model answers: "Yes, there is a logic issue on line 3, suggest modifying it to...".

This model presents three core pain points in productivity tool scenarios:

1. High Interaction Cost: Users need to copy error messages, switch windows, paste code, read the model's lengthy discourse,