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)

1. Understanding AI for Java Developers
2. Building Your First AI-Powered Java Application
3. RAG Systems: From Concept to Production
4. AI Observability and Testing
5. 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

1. Always include working code - Every article must have a GitHub repository
2. Use Spring AI when possible - It's the Java developer's gateway to AI
3. Include cost analysis - Help developers understand the financial implications
4. Address production concerns - Security, monitoring, error handling
5. 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