TRAE+Claude Code AI Product Enterprise-Level Application Practical Tutorial

TRAE+Claude Code AI Product Enterprise-Level Application Practical Course, focused on combining product thinking with AI tools, aimed at product managers in the AI era, based on the SIDE four-step closed-loop methodology, building a directly deployable enterprise-level AI product workflow.

The course starts from industry overview and course cases, guiding you step by step through the full environment setup: Claude Code, Gitee, Pencil MCP, TAPD MCP, TRAE workspace and plugin configuration, and sorts out common installation and configuration issues.

Guides you to master AI reading of MRDs and requirement discovery, learn information collection methods, and break down a one-sentence requirement into a clear requirement list; focuses on hands-on AI writing of PRD documents, mastering PRD standards and pitfalls to avoid, and using a 14-dimension scan to complete PRD audits.

Extends to learning page specification document writing and 6-dimension audits; uses Claude Code to quickly generate HTML prototypes, complete HTML-to-Pencil file conversion, create sitemaps, design review pages, and build product demo materials.

Also explains standard user story writing, AI batch generation of user stories, and connects the TAPD sync workflow to sync requirements and stories to the project management platform; includes AI to consolidate meeting discussion records and maintain the knowledge base, ensuring project knowledge assets are well retained.

Finally, reviews the complete project chain, SIDE deliverables across all stages, and product iteration roadmap, summarizes product pitfalls and lessons, and includes a graduation design practicum to test learning outcomes, truly integrating AI into the daily product workflow and greatly improving product manager output efficiency.

The course advances product methodology and toolchain operations within the same set of project cases, starting from environment configuration and practicing around real deliverables, gradually completing requirements, PRD, page specifications, prototypes, user stories, and project collaboration. Learners can become familiar with the process of AI reading MRD, batch-generating documents, audit checklists, and syncing with project management platforms through repeated hands-on practice, and check whether the outputs of each stage are complete during the review phase. For those who need to improve product delivery efficiency, standardize team collaboration, or prepare for AI product project implementation, this content can serve as a reference for building from tool usage to enterprise-level workflows.

Product managers and AI product managers who want to use AI to boost efficiency and build enterprise-level AI product workflows

Product newcomers who cannot write PRDs, cannot make prototypes, and are unfamiliar with TAPD project collaboration

Practitioners who want to learn TRAE, Claude Code, and the MCP toolchain to implement AI product delivery

R&D, testing, and project leads who want to understand product deliverables and connect AI collaboration workflows

Team leads who want to master the standardized SIDE closed-loop methodology and standardize enterprise product output

• ✅Understand AI product development trends and thoroughly grasp the SIDE four-step closed-loop product methodology
• ✅Independently complete the full environment configuration of TRAE, Claude Code, Gitee, Pencil MCP, and TAPD MCP
• ✅Learn AI parsing of MRDs and information collection, and turn vague requirements into structured requirement lists
• ✅Master AI writing of PRDs and 14-dimension scan audits of PRDs to avoid common PRD issues
• ✅Master page specification document writing and 6-dimension audits to standardize page delivery standards
• ✅Use Claude Code to generate prototypes, complete HTML-to-Pencil conversion, and create sitemaps and review demo pages
• ✅Master user story writing standards and AI batch generation of user stories
• ✅Connect the TAPD sync workflow to automatically sync user stories and complete project collaboration
• ✅Use AI to consolidate meeting decisions and build a product knowledge base for good knowledge management
• ✅Master complete project reviews and product iteration planning to form standardized enterprise delivery capabilities

• 1.Introduction to the AI Product Enterprise-Level Application Practical Course
• 2.Why AI product managers are the future
• 3.Understanding the course cases
• 4.SIDE methodology: the four-step closed loop from a product manager's perspective
• 5.One-sentence introduction to the course tools

• 6.AI software installation
• 7.Configuring Claude Code
• 8.Configuring Gitee
• 9.Pencil MCP configuration
• 10.TAPD MCP configuration
• 11.TRAE workspace and extensions
• 12.Common pitfalls and solutions

• 13.Using Claude Code once
• 14.Experiencing three core capabilities

• 15.Hands-on: using AI to read MRD and complete three tasks
• 16.Where product managers' requirements come from
• 17.Case breakdown: 6 core pain points
• 18.Three-step information gathering method
• 19.Hands-on: from one sentence to a requirement list

• 20.What Is a PRD: A Product Blueprint, Not a Reporting Document
• 21.Writing PRD Documents with AI
• 22.Key PRD Standards for Product Managers
• 23.Hands-on: Generating a PRD with AI
• 24.5 Common PRD Pitfalls
• 25.Using AI for 14-Dimension PRD Scanning
• 26.Hands-on: Auditing a PRD with AI

• 27.What Is a Page Specification Document
• 28.Standard Template for Page Specification Documents
• 29.Case Breakdown and Template Selection
• 30.Hands-on: Writing a Page Specification Document with AI
• 31.What Pages Most Easily Miss: 6-Dimension Scanning for Page Specification Documents
• 32.Hands-on: Auditing a Page Specification Document with AI

• 33.Why Product Managers Need to Know Prototyping
• 34.Prototyping Principles
• 35.Quick Start: Generating Prototypes with Claude Code
• 36.Case Prototype Breakdown
• 37.Hands-on: Creating a Homepage Prototype with AI
• 38.Why Convert HTML to Pencil
• 39.Detailed HTML to Pencil Workflow
• 40.Hands-on: Converting an HTML Prototype into a .pen File with AI

• 41.What Is a Sitemap
• 42.Generating a Sitemap with AI
• 43.Generating a Design Review HTML Page with AI
• 44.Hands-on: Generating a Sitemap + Review Page + Simulated 5-Minute Demo with AI

• 45.Why Developers Need User Stories
• 46.Standard User Story Template
• 47.User Story Overview
• 48.One-Click User Story Generation with AI
• 49.Hands-on: Writing a Set of User Stories for the Redemption + Verification Flow
• 50.Full TAPD Sync Workflow
• 51.How to Configure tapd-config.json
• 52.What a TAPD Story Looks Like
• 53.Hands-on: Syncing User Stories to TAPD with AI
• 54.User Story Sync Verification Checklist

• 55.Why Product Managers Should Write Good Discussion Records
• 56.Use AI to Capture Decisions
• 57.Knowledge Base Maintenance
• 58.Common Pitfalls Guide for Knowledge Management
• 59.Hands-on: Use AI to Capture a Discussion

• 60.Full Project Overview Review
• 61.SIDE Deliverables at Each Stage
• 62.Product Manager Pitfall Lessons Summary
• 63.Product Iteration Evolution Roadmap
• 64.8 Experience-Based Tips for Product Managers

• 65.AI Product Graduation Project