🛒 The Trae AI editor in-depth application solution for Chinese developers covers core capabilities such as AI-assisted programming, deep integration of beanbao large models, Agent mode, project-level code understanding, and Chinese developer experience optimization.

Trae AI editor in-depth application plan

Solution overview

This solution is aimed at software development teams and individual developers using Trae AI native IDE, and provides an end-to-end workflow from environment construction to independent development. The solution uses the Trae editor as the core tool, making deep use of its built-in 豆包 large model capabilities, covering six key aspects of requirement disassembly, conversational programming, multi-file automatic construction, SOLO independent development, code review and continuous optimization.

Core Value: Upgrade AI IDE from "code completion tool" to "intelligent collaborative developer". In Trae's Builder and SOLO modes, agents undertake long-span tasks from requirement understanding to code implementation, and developers focus on architectural decisions and quality control. For the Chinese developer market, Trae provides deep integration of the native Chinese interface and the beanbao model, lowering the language threshold for natural language programming.

Target users: Individual developers who develop in Chinese, small and medium-sized R&D teams, front-end/full-stack developers who build projects from scratch, and technical team leaders who want to incorporate AI coding into their daily processes.

Prerequisites:

• A macOS or Windows desktop computer with network access
• Have basic programming language knowledge and Git version management experience
• Download and install desktop IDE from Trae official website
• Have reasonable expectations for the reliability boundaries of AI programming - intelligently generated code requires manual review

Toolchain list

Tools	Role in this scenario	Required account levels	Alternatives
Trae	Core AI IDE, integrated conversational programming, Builder, SOLO three-level mode	Free version/Premium version	Cursor / GitHub Copilot
豆包	Trae has built-in large underlying models, providing Chinese understanding and generation	Free use with Trae	Claude / ChatGPT (Trae has built-in multiple models optional)
Cursor	Competitor reference for comparison and evaluation of Trae’s unique advantages	Free version/Pro version	—
GitHub Copilot	Reference to competing products to help understand the AI IDE market structure	Free version/Enterprise version	—
Claude	Assisted in-depth analysis and architectural design discussion	Free version/Pro version	ChatGPT
ChatGPT	Assisted requirement clarification and technical research	Free version/Plus version	Claude

Selection Points: Trae’s core differentiation lies in (1) native deep integration of ByteDance Beanbao large models, leading Chinese understanding and generation capabilities; (2) three-level autonomy progression from dialogue to SOLO, teams can gradually delegate authority based on trust; (3) localized experience for Chinese developers, and Chinese interaction can be used without additional configuration.

Preparation

Before launching a scenario, complete the following preparations to ensure a smooth workflow.

Account and environment

• [ ] Download and install Trae desktop IDE from Trae official website
• [ ] Register a Trae account and confirm that the built-in model quota is available
• [ ] Configure the Git environment (global user.name / user.email) and prepare the test warehouse
• [ ] Install the runtime environment that the project depends on (Node.js / Python / Go, etc., depending on the project technology stack)
• [ ] Optional: Prepare Claude or ChatGPT account as an auxiliary tool for in-depth architecture discussions

Project preparation

• [ ] Prepare a non-critical test project (personal project or open source Demo) to verify the quality of Trae changes
• [ ] Sort out the bottlenecks in the current development process (coding, refactoring, testing, and document dimensions)
• [ ] Align acceptance criteria for AI coding with team members – what level of human intervention is acceptable

Step-by-step guide

Step 1: Trae environment construction and skills understanding

⏱ Estimated time: 0.5–1 day 🎯 Goal: Complete the installation and configuration of Trae IDE, and master the basic operations of conversational programming, Builder, and SOLO modes ⚠️ Prerequisites: Desktop computer + Internet connection

Operation instructions

Trae’s pattern progressive design (Dialog → Builder → SOLO) is a key leverage point for the implementation of the solution. The first step is not to write code directly, but to understand the applicable boundaries and collaboration methods of each mode to avoid using wrong modes in wrong scenarios.

Specific operations

1. Installation and Initial Configuration

   • Download Trae desktop IDE from the official website and complete the installation
   • After activation, use your mobile phone number or email to register a Trae account
   • Enter the settings page and confirm that the bean bag model in the built-in model list has been activated
   • Optional: Add the API Key of Claude or ChatGPT as an alternative model (switch according to task difficulty)

2. Conversational programming mode experience

   • Open Trae's built-in chat panel (default right sidebar)
   • Question in natural language: "Write a Fibonacci sequence generating function in Python"
   • Observe whether the AI-generated code is directly inserted at the cursor position in the editor
   • Test modification request: "Change to async generator version"
   • Record: The response accuracy of dialogue mode in the "single file/partial change" scenario

3. Builder mode verification

   • Enter in the chat panel: "Use React + TypeScript to create a to-do component that supports additions, deletions, modifications and local storage"
   • Observe whether Builder automatically disassembles tasks and generates code files across files
   • Check whether the generated file structure is reasonable and whether the dependencies are complete
   • Record: Runnability rate of Builder mode in "medium complexity/multiple files" scenario

4. Initial exploration of SOLO mode

   • Create an empty directory and open it in Trae
   • Input: "Build a simple Markdown note-taking application that supports Markdown preview and file management"
   • Observe the steps and outputs of end-to-end advancement of SOLO model
   • Check whether a directly launchable project structure is generated
   • Record: SOLO mode task completion in the "build from scratch" scenario

5. Basic understanding of bean bag model capabilities

   • Perform the same code generation tasks using beanbag models and other available models in Trae
   • Compare the accuracy of understanding Chinese requirements and the consistency of coding style
   • Confirm the leadership of the bean bag model in the Chinese scenario

Access control and acceptance

• [ ] All three modes can be successfully triggered and produce expected code output.
• [ ] Conversation mode can correctly handle two typical requests: "local modification" and "code explanation"
• [ ] Builder mode can generate compilable/runnable multi-file project structures
• [ ] Confirm that the Chinese understanding accuracy of the bean bag model meets daily development needs
• [ ] Record the advantages, disadvantages and applicable boundaries of each model to form an internal "model selection guide" within the team

Step 2: Conversational Programming - Improving Daily Coding Efficiency

⏱ Estimated time: Continuous (throughout daily development) 🎯 Goal: Integrate Trae conversational programming into daily coding, replacing the traditional search engine + manual coding mode ⚠️ Prerequisites**: Step 1 is completed, and the third-speed mode has been explored

Operation instructions

Conversational programming is the mode Trae uses most frequently. The core lies not in "letting AI write how much code", but in "accelerating communication with natural language" - freeing developers from checking grammar documents, writing boilerplate code, debugging and troubleshooting, and focusing on architecture and business logic.

Specific operations

1. Code generation and completion

   • Write comments directly in the editing area to describe the required functions, and Trae automatically generates code
   • Paste the requirement text in the chat panel and AI will generate the complete code snippet
   • For complex logic, first generate the framework skeleton, and then add details layer by layer.

2. Code explanation and learning

   • Select an unfamiliar code fragment, right-click and select "Explain Code"
   • Let AI explain the logic line by line and mark the role of key variables
   • Use Trae to analyze the design patterns of the sample code of the open source library

3. Debugging Assistance

   • Paste the error message into the chat panel and let AI analyze the root cause
   • Select the exception stack + related code blocks at the same time and ask for possible repair directions
   • Let AI generate suggested locations and expected variable values for breakpoint debugging

4. Code Refactoring

   • Select the code block that needs to be refactored and describe the refactoring goal (such as "extract to utility function" "change to class method")
   • Review AI's reconstruction suggestions to confirm that they do not affect external interfaces
   • For large-scale refactoring, submit in steps and compare the differences at each step

5. Test Generation

   • Describe the behavioral boundaries of the function under test and let AI generate unit tests
   • For modules with insufficient test coverage, batch generation and completion are done
   • Review whether the boundary conditions of the test case are complete

Access control and acceptance

• [ ] At least 50% of the boilerplate code in daily coding sessions generated by Trae conversations
• [ ] Debugging efficiency: The time from the occurrence of an exception to the root cause location is shortened by more than 40%
• [ ] Improve unit test coverage by at least 15 percentage points from baseline
• [ ] The reconstructed finished product passes the original test suite and has no regression defects

Step 3: Builder mode - automatic construction of function modules

⏱ Estimated time: 0.5–2 hours each time (based on module complexity) 🎯 Goal: With the help of Builder mode, the development of medium-complexity functional modules can be compressed from "hours of manual coding" to "30 minutes of AI construction + human review" ⚠️ Prerequisites: Have mastered the conversational programming model and have basic trust in Trae’s agent behavior

Operation instructions

Builder is Trae's "automatic building" gear. The agent breaks down tasks as needed and generates code across multiple files. The core of this link is not "whether AI can be generated correctly the first time", but "how developers can efficiently review AI output" - it is necessary to establish a lightweight but effective review rhythm.

Specific operations

1. Standardization of requirement description

   • First write the functional requirements into a structured prompt: functional goals, input/output definitions, boundary conditions, dependencies
   • Enable Builder mode in Trae chat panel and paste the requirement description
   • Let the Builder output the task disassembly list first, and then execute it after confirming the rationality of the disassembly.

2. File generation and structure review

   • After Builder automatically creates the file structure, it first checks whether the directory organization is reasonable.
   • Check for redundant files or missing critical modules
   • Confirm that the naming convention is consistent with the project agreement

3. Code logic file-by-file review

   • Start the review from the core logic file to check whether the implementation matches the requirements
   • Check whether error handling and exception paths are complete
   • Confirm whether the type definition and interface signature are self-consistent

4. Integration Test

   • Run the code generated by Builder locally to verify basic functionality
   • Run the existing test suite of the project to confirm that there are no regressions
   • Write supplementary tests for new modules

5. Iterative Correction

   • After discovering the problem, describe the modification requirements in natural language and let the Builder correct it
   • There is no need to manually correct every error - give priority to letting AI correct it by itself, and humans only do final confirmation
   • For task types that frequently cause errors, optimize the way of describing requirements next time

Access control and acceptance

• [ ] The project structure generated by Builder was reviewed and passed, and no key files were missed.
• [ ] The core logic is implemented correctly and passes the basic function test
• [ ] All test suites have passed (zero regression)
• [ ] Human review time does not exceed 50% of AI generation time
• [ ] Record the "first pass rate" generated by each Builder to establish a quality baseline

Step 4: SOLO mode - independent development from zero to one

⏱ Estimated time: 2–8 hours per project (based on project complexity) 🎯 Goal: In SOLO mode, complete end-to-end development tasks (from requirement understanding to runnable applications) are led by agents ⚠️ Prerequisites: The Builder mode has been used proficiently and you have full confidence in the capabilities of the Trae agent

Operation instructions

SOLO is the mode with the highest degree of autonomy in Trae. The agent promotes a larger span of tasks from requirements to runnable applications end-to-end. This is the embodiment of the core value of the solution - developers shift from "writing code" to "defining requirements + reviewing outputs". However, the higher the degree of autonomy, the higher the review threshold, and a rhythm of progressive delegation of authority needs to be established.

Specific operations

1. Project-level requirements definition

   • Write a clear project requirements document (PRD): function list, user process, technology selection constraints
   • Enable SOLO mode in Trae, paste full PRD
   • Let SOLO output project architecture design and technology selection suggestions, and then execute them after confirmation
   • This step is very critical - the vaguer the requirement description, the greater the risk of SOLO deviation.

2. Phase delivery and acceptance

   • Require SOLO to be delivered in stages, pausing and reviewing after each stage is completed
   • Recommended splitting method: project initialization → data layer → business logic → front-end interface → integrated joint debugging
   • Give correction instructions after each stage of review before entering the next stage

3. Architecture and Code Review

   • Key review: Whether the module division is reasonable, whether the data flow is clear, and whether the dependency injection is correct
   • Check security: input verification, authentication logic, sensitive information processing
   • Verify there are no hard-coded credentials or debugging residue in the generated code

4. Automated verification

   • Require SOLO to generate both unit tests and integration tests
   • Run the test suite to confirm that coverage is up to standard
   • Manual smoke testing of critical paths

5. Documentation and Deployment

   • Let SOLO generate project README, API documentation, and environment configuration instructions
   • Generate Dockerfile or deployment script (if applicable)
   • Organize change lists in preparation for Git commits

Access control and acceptance

• [ ] The application generated by SOLO can run completely (the core function passes the smoke test)
• [ ] Test coverage ≥70% (new project standard)
• [ ] No security vulnerabilities: no hard-coded keys, SQL injection, XSS and other common issues
• [ ] The coding style is consistent with the project convention (naming, directory, file organization)
• [ ] The amount of human modifications after review ≤ 20% of the total code amount

Step 5: Deep integration of bean bag model - Chinese development experience optimization

⏱ Estimated time: 1–2 days (one-time optimization) 🎯 Goal: Make full use of Trae’s embedded 豆bag large model capabilities to optimize the interaction quality in Chinese development scenarios ⚠️ Prerequisites: Trae has been installed and can use the beanbag model

Operation instructions

The unique advantage of Trae compared to international competing products such as Cursor lies in its deep integration of large bean bag models. The beanbao model has been specially optimized in aspects such as Chinese semantic understanding, Chinese code comment generation, and Chinese technical document analysis. The purpose of this step is not as simple as "switching models", but to establish a set of usage strategies that "take advantage of the advantages of bean bags and avoid the disadvantages of bean bags".

Specific operations

1. Chinese requirements understanding comparison test

   • Prepare 10 sets of Chinese development requirements (such as: "Write a user login module to support mobile phone number verification code login and email password login")
   • Execute on the bean bag model and other built-in models respectively, and compare the degree of demand restoration
   • Create a "Beanbao first" Chinese scene list

2. Chinese annotation and document generation

   • Let the beanbao model automatically generate Chinese comments for existing code (function description, parameter meaning, return value explanation)
   • Generate Chinese README and technical documentation
   • Compare the difference in reading efficiency between Chinese and English annotations

3. Chinese technical Q&A

   • Frequently asked questions about the Chinese technical community (such as: "How to correctly manage the dependency array of React useEffect")
   • Compare the answer accuracy and case relevance of beanbags and general models
   • Accumulate best practices in Chinese Q&A

4. Multi-model switching strategy

   • Configure multi-model alternatives in Trae settings
   • Simple tasks (completion, formatting, annotation) → Beanbag model (low latency)
   • Complex tasks (architecture design, complex algorithms) → switch to stronger models
   • Establish a "Model Selection Cheat Sheet" within the team

Access control and acceptance

• [ ] The degree of restoration of the bean bag model in the Chinese scene reaches more than 90%
• [ ] Chinese annotation coverage reaches more than 80% of the core module
• [ ] Form and publish the "Best Practices for Using Beanbag Model" document within the team
• [ ] Multi-model switching strategy has been verified to be effective in actual development

Step 6: Code review and quality gatekeeping

⏱ Estimated time: 15–30 minutes per review (based on code volume) 🎯 Goal: Establish a special review mechanism for AI-generated code to ensure that the code quality of the program is acceptable ⚠️ Prerequisite: AI generated code has been incorporated into the daily development process

Operation instructions

The biggest risk of AI programming solutions is not "bugs in the AI-generated code", but "the team's review of the AI code is just a formality." This step specifically designs a review checklist based on the characteristics of AI-generated code, upgrading the review from "passing through" to "gatekeeping."

Specific operations

1. AI code special review checklist

   • Completeness: Are all functional requirements covered?
   • Consistency: Is the style of the new code consistent with the existing code base?
   • Boundary handling: Are error paths, null values, and concurrency conditions handled?
   • Security: Are there injection risks, hard-coded credentials, missing permissions?
   • Maintainability: Is the code clearly commented? Is dependency management correct?
   • Performance: Are there significant risks of N+1 queries, memory leaks, or infinite loops?

2. Difference comparison and line-by-line review

   • Use Trae’s built-in Diff view to review AI changes line by line
   • Review changes to multiple files generated by Builder/SOLO one by one
   • For unconfirmed changes, let Trae explain "why it was changed"

3. Automated access control integration

   • Configure CI/CD pipeline, new code must pass lint and test
   • Suggestion: Add the ai-generated tag to the PR tag of AI-generated code
   • Set up additional reviewer policy for AI code (at least 1 human reviewer)

4. Quality Baseline Tracking

   • Statistics on the defect rate of each round of AI-generated code (defects found during review/total lines of code)
   • Compare the defect density of human code and AI code
   • Separate statistics by mode (dialogue/Builder/SOLO) to establish a mode quality baseline

Access control and acceptance

• [ ] AI code review coverage 100% (required for every developer before submitting)
• [ ] Defect density of AI code ≤ defect density of human code in the same project
• [ ] All automated access control check items (lint + test) passed
• [ ] Output the AI code quality report at the end of each iteration and compare it with the baseline

Step 7: Project actual combat - complete Trae development cycle

⏱ Estimated time: 1–2 weeks (first complete project) 🎯 Goal: Run through the entire Trae process on a real project and verify the effect and cost of the solution in different scenarios ⚠️ Prerequisites: The first six steps have been completed, and the team has mastered the use of each mode of Trae

Operation instructions

This is the practical verification link at the end of the plan. Choose a real non-critical project (internal tools, prototype verification, open source demo), use Trae to lead the entire process from requirements to delivery, record the time-consuming and quality data of each link, and compare it with the historical baseline.

Specific operations

1. Project Selection

   • Choose non-critical projects with a cycle time of 1–2 weeks and familiar technology stacks
   • Recommended types: internal management backend, data visualization panel, CLI tool, API service
   • Unsuitable first projects: financial transaction systems, medical device software, projects involving sensitive data

2. Full process runthrough

   • Requirements analysis and dismantling → SOLO mode
   • Project skeleton generation → Builder mode
   • Function module development → Builder + Dialog Mix
   • Test generation → Conversation mode
   • Documentation and Deployment Scripts → Conversation Mode
   • Code review and refactoring → manual + Trae assistance

3. Data Collection

   • Record the actual time taken for each stage
   • Record the total number of lines of AI-generated code and the number of retained lines
   • Record the number and type distribution of defects found during the review
   • Record developers’ “trust score” for AI code (1–5 points)

4. Review and Baseline Establishment

   • Compare the efficiency differences between traditional development methods and Trae development methods
   • Analyze the advantages and disadvantages of each mode: In what scenarios should Builder be used? What scene cuts back to the conversation?
   • Output the "Trae User Manual v1.0" within the team

Access control and acceptance

• [ ] The project is delivered within the specified period, and the core functions are complete
• [ ] Retention rate of AI-generated code ≥ 70% (proportion that is not modified after review)
• [ ] Overall development efficiency increased by ≥ 50% compared to baseline (measured in man-hours)
• [ ] Team members’ trust score for Trae ≥ 4/5
• [ ] Output reusable Trae workflow templates and Prompt libraries

Expected results

Acceptance criteria

• [ ] The entire link of the solution has been verified (steps 1 to 7 are completed one by one)
• [ ] Team members can use Trae independently to complete daily development tasks
• [ ] The team has formed a standardized process for "AI code review"
• [ ] The quality baseline of each mode has been established, and the degree of delegation can be decided based on the baseline

Frequently Asked Questions and Troubleshooting

Q: What is the core difference between Trae and Cursor / GitHub Copilot? A: The core differences of Trae are (1) the native deep integration of ByteDance’s beanbao model, leading in Chinese understanding ability; (2) the three-level autonomy progression from dialogue → Builder → SOLO, the team can gradually delegate authority according to the degree of trust; (3) the localized experience for Chinese developers. The advantage of Cursor lies in richer model switching and the ecological accumulation of entering the market earlier. The advantage of GitHub Copilot lies in its deep integration with the GitHub ecosystem.

Q: How to choose between bean bag model and other built-in models? A: Recommended principles: Prioritize the beanbao model for Chinese requirement understanding, Chinese annotation generation, and Chinese technical Q&A; for complex architecture design and non-Chinese context tasks, you can switch to other built-in models. In Trae settings you can configure the multi-model alternative order.

Q: Is the code quality generated by SOLO mode reliable? A: The reliability of the SOLO model depends on two factors: the clarity of the requirements description and the rigor of the review process. The more vague the requirements, the greater the deviation. It is recommended that phased delivery and phased acceptance be mandatory in the early stages of the project, and that SOLO be allowed to take on larger-span tasks after the quality baseline is met.

Q: What is the biggest risk in implementing the plan? A: The biggest risk is not that there are bugs in the AI-generated code, but that the team’s AI code review is just a formality. The code generated by AI is most likely to have omissions in the three dimensions of "boundary condition processing", "security" and "performance", and a special review checklist must be included.

Q: How much learning cost does the team need to invest? A: It takes about 0.5–1 day to get started for the first time (step 1). It takes about 1–2 weeks to master the applicable scenarios and review rhythm of each mode (steps 2 to 4). It takes about 1–2 project cycles to form a mature Trae usage specification within the team.

Q: Is it applicable to large-scale existing projects? A: Applicable. However, it is recommended to first verify the context retrieval quality and change accuracy of Builder/SOLO in the existing code base on non-critical modules. For very large-scale projects, automatic changes to agents need to be accompanied by more stringent review and rollback processes, otherwise the benefits may be offset by rework costs.

Q: Does the plan have a free path? A: The Trae IDE ontology provides a free usage path, and some advanced models are exempt in the early stage. The specific free quota and subscription levels are subject to the official real-time page.

Implementation cycle and stage division

Advantages and Disadvantages of the Solution

Advantages

• Best Chinese experience: Trae deeply integrates the bean bag model, which is better than international competitors in Chinese demand understanding and Chinese annotation generation.
• Autonomy Progressive Design: Dialogue → Builder → SOLO three-speed mode, the team can gradually delegate power and reduce the risks caused by one-time full automation
• Low threshold to get started: The IDE itself is free, advanced models are free in the early stages, and the learning curve is gentle.
• Flexible switching of multiple models: Built-in multiple mainstream large models, switch according to task complexity to balance effect and cost
• Byte Ecological Blessing: Linked with ecosystems such as Doubao and Byte Cloud Services, with strong long-term scalability

Limitations

• Pricing strategy is not transparent: Free quota, subscription tiers, and model call billing are subject to the official real-time page. The team needs to continue to pay attention when selecting models.
• Enterprise-level capabilities to be confirmed: Privatized deployment, data compliance, and collaborative management capabilities need to be confirmed with the official on a case-by-case basis
• Large Project Context Retrieval: The accuracy of agent changes under extremely large code bases still needs to be verified
• Ecological maturity: Compared with Cursor and GitHub Copilot, Trae's community plug-in ecosystem and third-party integration are still under construction
• International scenario limited: The advantages of Doubao compared to Claude/GPT are not obvious in the English development scenario

Risks and Responses

Tool summary

Tools	slug	Roles in the scenario
Trae	trae	Core AI IDE
豆包	doubao	Built-in large model, the main force in Chinese scenes
Cursor	cursor	Competitive product reference
GitHub Copilot	github-copilot	Competitive product reference
Claude	claude	Assisted in-depth analysis
ChatGPT	chatgpt	Assistance requirements clarification

Summary

This solution takes the Trae AI native IDE as the core and uses its three modes of conversational programming, Builder automatic construction, and SOLO independent development, as well as the in-depth optimization of Doubao large models in Chinese scenarios to provide Chinese developers with a complete set of AI programming workflows from environment construction to project delivery.

The core design idea of ​​the solution is "progressive decentralization": first build developers' trust in AI through conversational programming, then let AI take on medium-complexity multi-file construction tasks through Builder, and finally achieve end-to-end independent development through SOLO. Review and quality gatekeeping are implemented throughout - special review checklist for AI code, defect rate tracking baseline, and automated access control to ensure that efficiency is improved without sacrificing code quality.

For teams that are evaluating AI IDE selection, it is recommended to first verify whether Trae matches the technology stack and working habits through the first phase of this plan (1 week of basic testing). For teams that have decided to use Trae, this solution provides a proven implementation path to help the team complete the full process coverage from entry to actual implementation within 8 weeks.