TRAE+Claude Code AI Product Enterprise-Level Application Practical Course, focused on combining product thinking with AI tools, building a directly deployable enterprise-level AI product workflow

Course Introduction

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.

Suitable For

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

Learning Outcomes

✅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

Course Catalog

01.1 Introduction to the AI Product Enterprise-Level Application Practical Course(1)

02.2, Why AI Product Managers Are the Future (1)

03.3, Understanding Course Cases (1)

04.4, SIDE Methodology: The Four-Step Closed Loop from a Product Manager's Perspective (1)

05.5, One-Sentence Introduction to Course Tools (1)

06.6, Al Software Installation (1)

07.7, Configuring Claude Code (1)

08.8, Configuring Gitee (1)

09.9, Pencil MCP Configuration (1)

10.10, TAPD MCP Configuration (1)

11.11, TRAE Workspace and Extension Plugins (1)

12.12, Common Pitfalls and Solutions (1)

13.13, Using Claude Code for the First Time (1)

14.14, Experiencing the Three Core Capabilities (1)

15.15, Hands-on: Using Al to Read the MRD and Complete Three Tasks (1)

16.16, Where Product Managers' Requirements Come From (1)

17.17, Case Breakdown: 6 Core Pain Points (1)

18.18, The Three-Step Information Collection Method (1)

19.19, Hands-on: From One Sentence to a Requirements List (1)

20.20, What Is a PRD: A Product Blueprint, Not a Reporting Document (1)

21.21, Using AI to Write PRD Documents (1)

22.22, Key PRD Product Manager Standards (1)

23.23, Hands-on: Using AI to Generate a PRD (1)

24.24, 5 Common PRD Pitfalls (1)

25.25, Using AI for a 14-Dimension PRD Scan (1)

26.26, Hands-on: Using AI to Audit a PRD (1)

27.27, What Is a Page Specification Document (1)

28.28, Standard Template for a Page Specification Document (1)

29.29, Case Breakdown and Template Selection (1)

30.30, Hands-on: Using AI to Write a Page Specification Document (1)

31.31, What Pages Most Easily Miss: A 6-Dimension Scan of the Page Specification Document (1)

32.32, Hands-on: Using AI to Audit a Page Specification Document (1)

33.33, Why Product Managers Need to Know How to Build Prototypes (1)

34.34, Prototyping Principles (1)

35.35, Quick Start: Using Claude Code to Generate a Prototype (1)

36.36, Case Prototype Breakdown (1)

37.37, Hands-on: Using AI to Create a Homepage Prototype (1)

38.38, Why HTML Needs to Be Converted to Pencil (1)

39.39, Detailed Process for Converting HTML to Pencil (1)

40.40, Hands-on: Using Al to Convert an HTML Prototype into a .pen File (1)

41.41, What Is a Sitemap (1)

42.42, Using AI to generate a sitemap (1)

43.43, Using AI to generate a design review HTML page (1)

44.44, Hands-on: Using AI to generate a sitemap + review page + simulate a 5-minute demo (1)

45.45, Why development needs user stories (1)

46.46, Standard user story template (1)

47.47, User story overview (1)

48.48, One-click user story generation with AI (1)

49.49, Hands-on: Write a set of user stories for the redemption + verification process (1)

50.50, Full TAPD sync workflow (1)

51.51, How to configure tapd-config.json (1)

52.52, What a TAPD Story looks like (1)

53.53, Hands-on: Sync user stories to TAPD with AI (1)

54.54, User story sync verification checklist (1)

55.55, Why product managers should write good discussion notes (1)

56.56, Capturing decisions with AI (1)

57.57, Knowledge base maintenance (1)

58.58, Common pitfalls guide for knowledge management (1)

59.59, Hands-on: Capture a discussion with AI (1)

60.60, Project overview review (1)

61.61, Deliverables of each SIDE phase (1)

62.62, Summary of product manager pitfalls and lessons learned (1)

53.63, Product iteration evolution roadmap (1)

64.64, 8 pieces of experience-based advice for product managers (1)

65.65, AI product graduation project (1)