Boost Your Development Workflow: Practical Tips for Using Cursor AI

Introduction

Recently our recycling team has been using Cursor to assist development and has iterated the AI development mode several times. I want to share some practical insights on AI programming.

AI programming does not replace a developer’s coding ability, nor does a few simple MCP configurations dramatically boost AI’s coding power. This article focuses on real‑world application, sharing ideas and process‑optimization experience to help you actually use AI in projects.

What Can Cursor Do for You?

Considering the monthly cost of over 100 CNY, using Cursor only for bean or mapper generation feels wasteful. Feeding the whole requirement document directly to it produces code far from our expectations. Detailed prompts are needed, but what counts as detailed?

Let Cursor help developers split the project – create an initial prompt framework.

Developers enrich the prompt content to help Cursor understand the project.

After development, let Cursor assist developers with regression verification.

1. Let Cursor Help You Better Split the Project

Since generating full code directly is difficult, we first ask AI to perform simpler tasks such as analyzing the requirement document.

The analysis generally involves two steps:

Analysis of the new process.

Analysis of the current status.

After these steps we expect Cursor to produce a preliminary functional analysis document based on the PRD, including entity classes, VO definitions, interface definitions, and database table schemas.

1.1 Analysis of New Process (PRD Document)

Cursor can summarize PRD content, but product PRDs often omit “obvious” context, limiting Cursor’s understanding. We can still use Cursor to extract functional points, accelerate our own comprehension, and perform bidirectional verification.

When feeding the PRD to Cursor, we should record its reasoning to avoid context length limits. Example prompt:

https://dashen.zhuanspirit.com/pages/viewpage.action?pageId=xxxxxxxx
Extract the content of the above link. Summarize the features related to 【XXXXX bill】, focusing on query list and detail flow only.
Analyze how many functional points and interfaces are needed.

Do not generate code yet; first analyze the requirements step by step.
Save your thoughts as markdown in .docs/技术设计.md

If the online PRD requires login, you can: Provide your local cookie so Cursor can access the document. Use MCP (method not expanded here).

Another example prompt to clarify scope:

We do not need data synchronization or persistence; all data is ready, we only need queries.
Help design the query parameters and return values for this interface.

Below is the basic flow of PRD analysis with Cursor:

Remember not to let Cursor teach you how to do the project; let it assist each step and manually adjust any generated artifacts that do not meet business naming conventions.

If you make many manual changes, start a new conversation with Cursor to avoid stale memory.

Sample excerpts of the generated functional analysis document are shown below:

1.2 Analysis of Existing Code and Impact Scope

Cursor is closer to code than developers. With its help we can better map existing business processes, starting from the changed functional points and covering data management, interaction flows, etc. We commonly use the Mermaid Preview and Markdown Preview Enhanced plugins.

Flowchart generation plugins

After installing the plugins and restarting Cursor, we can generate flowcharts with prompts such as:

com.xxx.xxx.xxx.xxx.facade.SxxxxxxxxFacade #getxxxxxx
Generate a .md business flowchart for this method.

This helps determine the impact range of code changes.

2. Help Cursor Better Understand the Project and Generate Code

We aim to provide Cursor with detailed context so it can generate code that meets our expectations. The main goals are:

Adjust AI naming conventions and code generation locations (use Rules to simplify).

Adjust AI‑used utility and service classes.

Adjust AI logic details (validation rules, decision order, etc.).

Adjust AI business details (extract original PRD text, describe missing flows, or let Cursor reserve methods for manual completion).

The resulting detailed MD document is illustrated below:

This document is understood by both us and Cursor, forming a solid basis for generating code that satisfies the requirements.

The overall logic flow for code generation with Cursor:

3. Cursor Helps Developers with Regression Verification

Beyond development assistance, Cursor also supports post‑development quality assurance in two areas:

Change impact assessment.

Functional verification.

3.1 Change Scope Organization

Run the following command to list changed files and save to cr.diff:

git diff origin/master > cr.diff

3.2 Basic Code Review

Guide Cursor with a prompt containing review rules, for example:

Scan the diff file for differences. Review rules:
1. Method body length < 100 lines (excluding blanks and comments).
2. Enums must have at least two attributes: code, name, and a method to get enum by code.
3. Interface returns false – ensure front‑end handles it.
4. Exceptions must be logged.
5. All public methods log input parameters.
6. All public methods log completion.
7. RPC calls must be logged.
8. All methods need method‑level comments.
9. When re‑throwing in catch, preserve original exception.
10. Do not use @Deprecated members.
11. Constants need clear usage comments.
12. @Transactional methods must specify rollback exceptions; read‑only methods set readOnly=true.
13. Use StringBuilder for >3 string concatenations.
14. Check for compatibility‑breaking changes.
15. Detect circular calls.
16. Ensure idempotency for new page submissions and payments.
17. Handle concurrency, transaction conflicts, and data overwrite for repeated calls or MQ duplicate consumption.
18. Prefer precise field updates over whole‑object updates.
19. Detect duplicate code.
20. Verify count‑system adjustments.
21. Null‑check during data exchange.
22. Validate pre‑states for asset‑loss flows.
23. Ensure overall transaction consistency; provide compensation if not.
24. Handle intermediate states for async callbacks.
25. Do not expose configuration rules to operations.
26. Return empty list instead of null.
27. Primary keys must be auto‑increment or distributed IDs.
28. Ensure indexes on database tables.
29. Log alerts for exception scenarios.
Iterate each rule, output violations with file locations, and wait for confirmation before fixing.

3.3 Functional Assistance Verification

After code generation and initial review, use Cursor to generate self‑test case lists covering normal flows, exception inputs, and state checks:

Based on current code changes, list self‑test functional points and boundary cases, including normal flow, exception input, and state validation. Output as a table with case ID, scenario description, and expected result.

This step reinforces logic confirmation and reduces later regression cost.

Conclusion

This article shared our team’s experience with AI‑assisted programming, highlighting how to efficiently use Cursor across requirement analysis, current state mapping, technical design, code generation, and testing verification.

In the AI era, a developer’s core competence shifts from “knowledge possession” to “question formulation”. Tools like Cursor are not replacements but collaborators that boost requirement understanding, design, and code quality through clear context and guided prompts, achieving an efficient, high‑quality development loop.