Boost Your Java Development with AI: 5 Powerful Wenxin Code Tips

A Java developer with four years of experience introduces five useful Wenxin Code tips, focusing on the Zulu AI coding agent and the Chat assistant, to enhance AI‑assisted development.

Zulu Tuning Guide: Making Generated Code More Usable

1. Provide context with # – Large language models rely on preceding context to predict the next token. In development, context includes the current file, project structure, dependencies, and variable scopes. Zulu supports file, folder, and project‑level context, which can be invoked using the # operator.

Step 1: Use the # operator to open the context menu.

Step 2: Select one or multiple files or directories. If none is chosen, the current project is used as the default context.

Step 3: Describe the desired task in natural language. Example: ask Zulu to implement a cache deserialization function for Redis in Cache.java. Zulu reads the selected context and modifies the file accordingly.

2. Automate command execution – Zulu can detect the project's framework, tech stack, and file structure, then generate and run terminal commands directly in the IDE. This is especially useful for scripting languages like Python or JavaScript, allowing the AI to handle environment setup and dependency installation without leaving the IDE.

3. Rule constraints – When no rule file is provided, Zulu may generate code that deviates from project standards. By defining custom rules in Wenxin Code, you can guide Zulu’s output. The rule sections include:

3.1 Coding environment – Specify the language (Java) and frameworks (Spring, Spring Boot, Spring Security, MyBatis, etc.) so Zulu generates code that conforms to the project’s stack.

# 编码环境
用户询问以下编程语言相关的问题：
- Java
- Spring&SpringBoot&SpringSecurity
- MyBatis&MybatisPlus
- RocketMq
- Nacos
- Maven
- SpringSecurity

3.2 Code implementation guide – Provide detailed SOPs such as naming conventions (DO, PO, DTO), DDD patterns, MapStruct usage, RESTful API prefixes, and a unified BaseResp response wrapper.

# 代码实现指南
1. 项目使用 DDD 的方式来实现代码，你需要注意如下几点：
  1. 领域层和仓库层的入参都要使用 DO、仓库层的实体对象需要添加 PO 的后缀
  2. Application 或 Service 层的输出必须是 DTO，接口层的返回可以是 DTO 也可以自己定义 VO
  3. 每一层对应的对象都需要添加对应的后缀，并且后缀要全大写，如 UserPO、UserDO、UserDTO
  4. 使用 MapStruct 完成层间转换
  5. 接口遵循 RestFul 风格，前端使用 /api/fe/v1/，OpenAPI 使用 /api/open/v1/，小程序使用 /mini-program/v1/
  6. 所有接口返回 BaseResp 对象

3.3 History recording – After each generation, Zulu can log the changes to a .cursor-history file with timestamps, changed content, and affected files, stored in reverse chronological order.

# 历史记录
2025-11-11 10:10:10
变更内容如下：
1. 增加用户模块
2. 修改用户管理内容
3. 增加用户内容
涉及文件为：
User.java
UserService.java

Chat Hidden Tricks: More Flexible Coding Interaction

1. Inline Chat – Select a code snippet and press Ctrl+I to invoke an inline conversation with Wenxin Code, allowing quick optimization or adjustments of the selected code.

2. Git Commit shortcut – After completing a feature, click the Git Commit button; Comate automatically analyzes code changes and generates a detailed commit message, improving efficiency and commit quality.

Practical Cases

1. Community auto‑sign‑in script – Using Zulu, a Python script that signs in and participates in a lottery is generated in three steps: write a natural‑language prompt with API definitions, let Zulu create the script files, and execute the generated commands.

2. Intent recognition service under constraints – Demonstrates how to embed custom rules into the prompt so that Zulu generates a service that first matches local rules, falls back to a third‑party LLM if needed, and returns results via an OpenAPI endpoint.

In conclusion, Wenxin Code’s Zulu and Chat agents provide strong automation for code generation, context‑aware editing, rule‑based constraints, and seamless IDE integration, making AI‑assisted development a natural part of the Java developer’s workflow.