Full Stack • Java • System Design • Cloud • AI Engineering

AI Learning Path

A clean, ordered AI learning path starting with the Spring AI Guide and continuing through foundations, engineering, agents, architecture, MCP, projects, design patterns, AIOps, and enterprise projects.

AI Learning Path

Follow this path from practical Spring AI implementation into AI foundations, engineering, agents, enterprise architecture, MCP, design patterns, operations, and full enterprise projects.

Module Order

Order Module Articles Focus
1 Spring AI Guide 10 A practical Spring AI guide for chat assistants, RAG, tools, agents, MCP integration, and enterprise AI platforms.
2 AI Foundations 31 Core AI, machine learning, deep learning, generative AI, embeddings, vector databases, RAG, agents, and enterprise AI architecture.
3 AI Engineering 30 Engineering practices for building production AI applications, including memory, search, tools, structured output, observability, security, and deployment.
4 AI Agents 20 Agent architecture, single-agent and multi-agent systems, memory, orchestration, state, security, monitoring, and cost optimization.
5 Agentic AI 15 Agentic AI patterns for reasoning, planning, reflection, collaboration, delegation, supervision, scheduling, and production architecture.
6 Enterprise AI Architecture 15 Enterprise AI platform design with multi-LLM routing, gateways, governance, compliance, audit logging, rate limiting, failover, and prompt versioning.
7 Model Context Protocol 10 MCP fundamentals, clients, servers, tools, resources, prompts, Java servers, Spring AI integration, and enterprise MCP architecture.
8 Spring AI Projects 15 Hands-on Spring AI projects for chat, RAG, agents, SQL, GitHub, Jira, Slack, HR, code review, documentation, and enterprise platforms.
9 AI Design Patterns 15 Reusable AI architecture patterns including RAG, ReAct, reflection, tools, planners, memory, evaluators, routers, guardrails, and human approval.
10 AIOps 10 Operational AI practices for monitoring, logging, metrics, tracing, CI/CD, deployment, canary release, rollback, observability, and cost dashboards.
11 Enterprise AI Projects 10 Enterprise AI project blueprints for ChatGPT-style apps, RAG platforms, support, banking, insurance, HR, code review, knowledge portals, and microservices.

Path Map

flowchart LR
    A["Spring AI Guide"]
    B["AI Foundations"]
    C["AI Engineering"]
    D["AI Agents"]
    E["Agentic AI"]
    F["Enterprise AI Architecture"]
    G["Model Context Protocol"]
    H["Spring AI Projects"]
    I["AI Design Patterns"]
    J["AIOps"]
    K["Enterprise AI Projects"]

    A --> B
    B --> C
    C --> D
    D --> E
    E --> F
    F --> G
    G --> H
    H --> I
    I --> J
    J --> K

Spring AI Guide

A practical Spring AI guide for chat assistants, RAG, tools, agents, MCP integration, and enterprise AI platforms.

  1. Spring AI Introduction: Features, Architecture, and Data Flow
  2. Build a Chat Assistant with Spring AI: Step-by-Step Guide
  3. RAG with Spring AI and PGVector: Step-by-Step Guide
  4. Build a PDF Knowledge Assistant with Spring AI
  5. Function Calling with Spring AI: Real Use Case Guide
  6. Build a Multi-Agent System with Spring AI
  7. MCP Integration with Spring AI: Step-by-Step Guide
  8. Build a SQL Generator with Spring AI
  9. Build a Code Generator with Spring AI
  10. Build an Enterprise RAG Platform with Spring AI

AI Foundations

Core AI, machine learning, deep learning, generative AI, embeddings, vector databases, RAG, agents, and enterprise AI architecture.

  1. What Is Artificial Intelligence (AI)?
  2. Types of AI: ANI, AGI, and ASI Explained
  3. AI vs Machine Learning vs Deep Learning
  4. Data: The Fuel of AI
  5. Supervised Learning Explained
  6. Unsupervised Learning Explained
  7. Reinforcement Learning Basics
  8. Features, Labels & Training Data Explained
  9. Model Training, Validation & Testing Explained
  10. Regression Algorithms Explained
  11. Classification Algorithms Explained
  12. Decision Trees and Random Forest Explained
  13. Clustering and K-Means Explained
  14. Recommendation Systems Explained
  15. Bias, Variance and Overfitting Explained
  16. Model Evaluation Metrics Explained
  17. Neural Networks Explained
  18. Activation Functions Explained
  19. Forward Propagation and Backpropagation Explained
  20. CNN for Image Processing
  21. RNN and LSTM for Sequence Data
  22. What Is Generative AI?
  23. Transformer Architecture
  24. Embeddings Explained
  25. Tokenization Explained
  26. Vector Databases
  27. Large Language Models (LLMs)
  28. Prompt Engineering
  29. RAG: Retrieval Augmented Generation
  30. AI Agents and MCP
  31. Enterprise AI Architecture

AI Engineering

Engineering practices for building production AI applications, including memory, search, tools, structured output, observability, security, and deployment.

  1. Spring AI Introduction
  2. LangChain4j Introduction
  3. Conversation Memory
  4. Streaming Responses
  5. Semantic Search
  6. Hybrid Search
  7. Chunking Strategies
  8. Embedding Models
  9. Reranking Techniques
  10. Structured Output
  11. JSON Mode
  12. Tool Calling
  13. Vision Models
  14. OCR With AI
  15. PDF QA System
  16. SQL Generation
  17. Code Generation
  18. AI Testing
  19. AI Caching
  20. AI Observability
  21. AI Logging
  22. AI Rate Limiting
  23. AI Security
  24. AI Authentication
  25. AI Gateway
  26. AI REST APIs
  27. AI Performance Tuning
  28. AI Production Best Practices
  29. AI Monitoring
  30. AI Deployment

AI Agents

Agent architecture, single-agent and multi-agent systems, memory, orchestration, state, security, monitoring, and cost optimization.

  1. What Is An AI Agent
  2. AI Agent Architecture
  3. Single Agent System
  4. Multi Agent System
  5. Planner Agent
  6. Executor Agent
  7. Reviewer Agent
  8. Research Agent
  9. Coding Agent
  10. Testing Agent
  11. Documentation Agent
  12. Autonomous Agent
  13. Human In The Loop
  14. Agent Memory
  15. Agent Communication
  16. Agent Orchestration
  17. Agent State Management
  18. Agent Security
  19. Agent Monitoring
  20. Agent Cost Optimization

Agentic AI

Agentic AI patterns for reasoning, planning, reflection, collaboration, delegation, supervision, scheduling, and production architecture.

  1. Agentic AI Introduction
  2. Re Act Pattern
  3. Reflection Pattern
  4. Tree Of Thoughts
  5. Graph Of Thoughts
  6. Planning And Reasoning
  7. Workflow Orchestration
  8. Agent Collaboration
  9. Agent Delegation
  10. Supervisor Agent
  11. Manager Agent
  12. Multi Step Reasoning
  13. Long Term Memory
  14. Agent Scheduling
  15. Agentic AI Production Architecture

Enterprise AI Architecture

Enterprise AI platform design with multi-LLM routing, gateways, governance, compliance, audit logging, rate limiting, failover, and prompt versioning.

  1. Enterprise AI Architecture Overview
  2. Multi LLM Architecture
  3. LLM Routing
  4. Enterprise RAG
  5. AI Gateway
  6. AI Proxy
  7. AI Governance
  8. AI Compliance
  9. AI Audit Logging
  10. AI Cost Tracking
  11. AI Rate Limiting
  12. AI Load Balancing
  13. AI Failover
  14. Prompt Versioning
  15. AI Platform Architecture

Model Context Protocol

MCP fundamentals, clients, servers, tools, resources, prompts, Java servers, Spring AI integration, and enterprise MCP architecture.

  1. MCP Introduction
  2. MCP Architecture
  3. MCP Client
  4. MCP Server
  5. MCP Tools
  6. MCP Resources
  7. MCP Prompts
  8. Building An MCP Server In Java
  9. Spring AI With MCP
  10. Enterprise MCP Architecture

Spring AI Projects

Hands-on Spring AI projects for chat, RAG, agents, SQL, GitHub, Jira, Slack, HR, code review, documentation, and enterprise platforms.

  1. Build A Chat GPT Clone
  2. Build A RAG System
  3. Build A Banking Agent
  4. Build An Insurance Agent
  5. Build A Customer Support Agent
  6. Build A SQL Agent
  7. Build A Git Hub Agent
  8. Build A Jira Agent
  9. Build A Slack Agent
  10. Build An HR Agent
  11. Build A Code Review Agent
  12. Build A Documentation Agent
  13. Build A Knowledge Assistant
  14. Build A Financial Advisor Agent
  15. Build An Enterprise AI Platform

AI Design Patterns

Reusable AI architecture patterns including RAG, ReAct, reflection, tools, planners, memory, evaluators, routers, guardrails, and human approval.

  1. RAG Pattern
  2. Re Act Pattern
  3. Reflection Pattern
  4. Tool Pattern
  5. Planner Pattern
  6. Memory Pattern
  7. Evaluator Pattern
  8. Router Pattern
  9. Agent Pattern
  10. Multi Agent Pattern
  11. Supervisor Pattern
  12. Workflow Pattern
  13. Guardrail Pattern
  14. Human Approval Pattern
  15. AI Cache Pattern

AIOps

Operational AI practices for monitoring, logging, metrics, tracing, CI/CD, deployment, canary release, rollback, observability, and cost dashboards.

  1. AI Monitoring
  2. AI Logging
  3. AI Metrics
  4. AI Tracing
  5. AI CI/CD
  6. AI Deployment
  7. AI Canary Release
  8. AI Rollback
  9. AI Observability
  10. AI Cost Dashboard

Enterprise AI Projects

Enterprise AI project blueprints for ChatGPT-style apps, RAG platforms, support, banking, insurance, HR, code review, knowledge portals, and microservices.

  1. Enterprise Chat GPT
  2. Enterprise RAG Platform
  3. Customer Support Platform
  4. Banking AI Assistant
  5. Insurance AI Assistant
  6. HR AI Assistant
  7. Code Review Platform
  8. Knowledge Portal
  9. Documentation Generator
  10. Enterprise AI Microservices

Completion Path

  1. Complete the Spring AI Guide first.
  2. Continue through each module in the order shown above.
  3. Use each module home page as the source of truth for Previous and Next navigation.
  4. Finish with enterprise projects that combine Spring AI, RAG, agents, governance, operations, and platform architecture.
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