- Published on
- Authors
- Name
- Gary Huynh
- @gary_atruedev
AI Engineering Category
This directory contains comprehensive AI engineering content specifically designed for traditional developers who want to integrate AI into their applications.
Category Focus
- AI for Traditional Developers: Making AI accessible to Java/Spring developers
- Production-Ready Examples: All code examples are deployable and tested
- Practical Integration: Focus on adding AI to existing applications
- Cost-Conscious: Always consider performance and cost implications
- Security-First: Address AI-specific security concerns
Content Structure
AI Fundamentals Series (5 parts)
- Understanding AI for Java Developers
- Building Your First AI-Powered Java Application
- RAG Systems: From Concept to Production
- AI Observability and Testing
- Scaling AI Applications
Practical Tutorials
- Adding Semantic Search to Spring Boot
- Building AI Code Review Assistants
- Implementing Smart Documentation
- Real-Time AI Features
Architecture Patterns
- Event-Driven AI with Kafka
- Hybrid Architectures
- AI Security Patterns
- AI Pipeline Design
Writing Guidelines
- Always include working code - Every article must have a GitHub repository
- Use Spring AI when possible - It's the Java developer's gateway to AI
- Include cost analysis - Help developers understand the financial implications
- Address production concerns - Security, monitoring, error handling
- Progressive complexity - Start simple, build to advanced
Target Audience
- Java developers with 2+ years experience
- Spring Boot users wanting to add AI features
- Architects evaluating AI integration
- Teams building production AI systems
Success Metrics
- Each article should be 2,500+ words
- Include 3+ working code examples
- Cover security and cost considerations
- Provide production deployment guidance