AI Prompt Engineering & Full Stack Development Roadmap
Document Positioning and Usage Instructionsβ
Target Audience: Developers with at least one programming language foundation (Python/Java/JavaScript) who wish to systematically master AI capabilities.
Core Goal: Centering on Generating High Quality Prompts, help developers grow from tool users to Full Stack Engineers capable of designing enterprise-grade AI systems.
Learning Path: Level 1 (Tool Driven) β Level 2 (RAG Application) β Level 3 (Agent Architecture) β Level 4 (Production Deployment), each phase 2-3 weeks, total 9-12 weeks.
Core Philosophyβ
Design Principlesβ
- β Architecture Patterns Priority over Tool Learning: Understand Sidecar Pattern, Async Decoupling, Structured Output and other architecture patterns.
- Extensions over Deep Dives: Treat platforms like Dify as black boxes, focus on extension and integration.
- β Evaluation Shift Left: Establish Golden Dataset and Automated Evaluation from early stage.
- Observability: Establish complete Agent behavior tracking and quality monitoring system.
- β Dual Track Parallel: Master both rapid delivery tools (Dify) and deep customization capabilities (LangGraph).
Technical Route Dual Track Systemβ
- β Productivity Track (80% Scenarios): Dify, Ollama, MCP ββ Quickly solve general needs.
- Hard Power Track (20% Core Challenges): PyTorch, LangGraph, VectorDB ββ Conquer complex problems.
Capability Evolution Pathβ
Quick Navigationβ
| Phase | Cycle | Core Goal | Priority |
|---|---|---|---|
| β Level 1: AI-Native Workflow | Weeks 1-2 | Establish AI-first development habits, build local AI infrastructure | Must Learn First |
| β Level 2: RAG App Development | Weeks 3-5 | Master Retrieval-Augmented Generation (RAG), understand heterogeneous system architecture | Core Capability |
| Level 3: Agent Architecture | Weeks 6-8 | Master Intelligent Decision & Routing, establish observability and auditing capability | Advanced |
| Level 4: Full Stack Landing | Weeks 9-12 | Complete Frontend & Backend Full Stack Delivery, master model fine-tuning and production deployment | Senior |
| Prompt Quality Evaluation & Summary | - | Prompt quality standards, skill combinations, interview crash list | Reference |
Critical Success Factorsβ
- β Architecture Patterns Priority over Tool Learning: Understand Sidecar Pattern, Async Decoupling, Structured Output and other architecture patterns.
- Extensions over Deep Dives: Treat platforms like Dify as black boxes, focus on extension and integration.
- β Evaluation Shift Left: Establish Golden Dataset and Automated Evaluation starting from Level 2.
- Observability: Establish complete Agent behavior tracking and quality monitoring system.
- β Dual Track Parallel: Master both rapid delivery tools (Dify) and deep customization capabilities (LangGraph).
Your Java/React Experience determines how Stable you can build the system (Complex Architecture Capability), while your AI Tool Driving Capability determines how Fast you can run (Development Efficiency).
Adding these two together makes a true Full Stack AI Engineer.