30 Proven Prompt Templates to Unlock Tongyi Lingma’s Full Potential

Why Your Tongyi Lingma Feels "Useless"

Even after installing the plugin, many users struggle because the prompt wording is incorrect . Tongyi Lingma is a large model trained for programming tasks, so clear, detailed prompts produce accurate results .

Three Interaction Modes

Code Completion (Inline) : Write comments directly in the editor for simple function generation or line‑level completion.

Intelligent Q&A : Use the right‑hand chat panel for concept explanations, code reviews, or architectural discussions.

File Editing / Agent Mode : Switch the chat panel to "File Editing" for multi‑file modifications, full‑project scaffolding, or complex task automation.

Principle: Use inline completion for simple tasks; use the chat panel for complex ones because it runs a larger‑parameter model and yields higher quality output.

Full‑Scene Prompt Templates (30 Examples)

① Code Generation

Inefficient prompt: 帮我写一个用户登录接口 Efficient prompt:

请用 Python Flask 框架编写一个用户登录接口：
<ul><li>路由：POST /api/login</li><li>请求参数：username（邮箱格式）、password</li><li>密码存储：bcrypt 加密，校验时对比哈希值</li><li>登录失败超过5次，锁定账号30分钟</li><li>成功返回 JWT Token（有效期2小时）</li><li>失败返回具体错误码和提示</li></ul>

Other scenarios include inline comment‑driven completion (Java example) and data‑processing function requests (Python pandas cleaning).

② Code Explanation

Ask the model to explain selected code in plain language, or request targeted explanations such as distributed‑lock implementation, over‑sell prevention, and transaction rollback handling.

③ Unit Testing

Provide language, framework, and coverage requirements, then ask for test code. Example: generate JUnit5 + Mockito tests covering normal flow, null inputs, insufficient balance, and frozen accounts.

④ Code Review & Optimization

Performance optimization: request line‑by‑line explanations of expected impact.

Style and safety checks: single‑responsibility, naming, magic numbers, exception handling.

Security audit: SQL injection, XSS, hard‑coded secrets, permission checks with risk levels.

Refactoring suggestions: extract duplicated logic, apply design patterns, reduce cyclomatic complexity.

⑤ Documentation Generation

Java: generate JavaDoc with @param, @return, @throws, and a one‑sentence summary.

Python: produce Google‑style docstrings covering description, Args, Returns, Raises, and an example.

C++: create Doxygen comments with @brief, @param, @return, @note, and time‑complexity notes.

⑥ Debugging & Error Analysis

Paste stack traces or logs, describe expected vs. actual behavior, and ask the model to locate logical errors, explain why the original code fails, and propose fixes.

⑦ Multi‑File Editing (File‑Edit Mode)

Give directory‑wide instructions, such as changing all Axios timeout values from 5000 ms to 10000 ms and inserting a console log before each request interceptor.

⑧ Programming Agent Tasks

Define a complete project, e.g., a Go gRPC service with HTTP‑gateway support, health‑check endpoint, and runnable via go run; or add a role‑based permission module to an existing React app, specifying routing, token handling, and config updates.

Five General Tips to Double Output Quality

Provide concrete examples rather than abstract rules.

Specify a role (e.g., "10‑year Java backend architect") to deepen the model’s reasoning.

Use multi‑turn conversations: start with a basic version, then incrementally add requirements.

Save files (Ctrl+S) before issuing file‑edit prompts so the model sees the latest structure.

Leverage @workspace to query the whole project, such as authentication flow or payment‑related modules.

One‑Sentence Summary

Tongyi Lingma already packs the full DeepSeek V3/R1 model; the only reason it feels ineffective is insufficient prompt detail.

Core Principle: Write clear, complete prompts—whether for inline completion, chat‑panel tasks, or file‑edit operations—to unlock the model’s full capability.