Large‑Model AI: Revolutionizing Software Development Tools

Introduction

Today’s theme examines the development and application of large‑model technology in the R&D field, questioning how these models can not only assist coding but also fundamentally transform workflows and critical processes.

Agenda

Intelligent R&D tool development

Enterprise landing experience

Developer practice

Q&A session

Intelligent R&D Tool Development

We first review the evolution and direction of intelligent R&D tools. Early AI‑assisted tools such as GitHub Copilot appeared about two and a half years ago, initially offering code‑completion suggestions. Over time, Copilot expanded to include chat‑based features like language conversion and unit‑test generation.

1. Background and Deployment Demands

With the rapid advancement of large models, related products are quickly evolving. From simple code assistance, tools now collaborate with developers, allowing entire tasks to be handed over to AI. The shift moves from mere code completion to generating code based on requirements, supporting the whole engineering workflow.

2. Go Fat: Larger‑Scale Code Completion

The “Go Fat” trend refers to the increasing scale of code‑completion suggestions. Traditional completion inserts a gray line based on model predictions, reducing keystrokes to a single Tab press. Modern tools aim to expand this capability while preserving user habits, delivering more extensive and context‑aware suggestions.