AI Technology Roadmap

A comprehensive learning path for building production-ready AI applications with modern frameworks and tools.

🗺️ Learning Path

📚 Technology Stack

Foundation Layer 🏗️

Essential skills and tools for AI development:

Core Skills

• Python Programming (Resources)
• API Integration (LLM APIs Guide)
• Prompt Engineering (Patterns & Techniques)

Development Environment

• VS Code + Python Extensions
• Jupyter Notebooks
• Git & Version Control

Framework Layer 🦜🔗

Primary frameworks for building LLM applications:

LangChain Ecosystem

• LangChain Core - Framework fundamentals
  • Components - Models, prompts, parsers
  • Chains - Sequential operations
  • Memory - State management

LangGraph Advanced

• LangGraph - Stateful workflows
  • State Management - TypedDict & persistence
  • Workflows - Graph-based patterns
  • Agents - Multi-actor systems

Data Layer 🗄️

Vector databases and retrieval systems:

Vector Databases

• Development: Chroma, FAISS
• Production: Pinecone, Qdrant
• Enterprise: Weaviate

Document Processing

• Text Splitters - Chunking strategies
• Embeddings - OpenAI, Sentence Transformers
• Metadata Management - Enrichment and filtering

Application Layer 🚀

Complete AI application patterns:

RAG Systems

• Basic RAG - Document Q&A
• Advanced RAG - Multi-hop reasoning
• Conversational RAG - Chat with documents

AI Agents

• Tool Agents - Function calling
• Multi-Agent - Collaborative systems
• Human-in-the-Loop - Supervised workflows

Specialized Applications

• Text-to-SQL - Database querying
• Code Generation - Development assistants
• Content Creation - Automated writing

Production Layer ⚡

Deployment and optimization:

API Development

• FastAPI - RESTful APIs
• Streamlit - Quick demos
• Gradio - ML interfaces

Performance

• Caching Strategies
• Batch Processing
• Async Operations

Security & Monitoring

• API Key Management
• Rate Limiting
• Cost Tracking

🎯 Quick Start Paths

For Beginners 🌱

1. Start Here: Python Basics
2. LLM APIs: OpenAI Integration
3. First App: LangChain Introduction

For Developers 💻

1. Framework: LangChain Core
2. Vector DB: Choose Database
3. RAG System: Build Q&A App

For Advanced Users 🚀

1. LangGraph: Advanced Workflows
2. Production: Deployment Guide
3. Optimization: Performance Tuning

🔧 Current Content Organization

LangChain Section

Location: /ai-tech/langchain/

• Main Guide - Comprehensive framework overview
• Introduction - Quick start guide
• Components - Models, prompts, parsers
• Chains - Sequential operations
• Memory - State management

LangGraph Section

Location: /ai-tech/langgraph/

• Introduction - Framework concepts
• State Management - TypedDict & persistence
• Workflows - Graph patterns
• Agents - Multi-actor systems

Specialized Topics

• Text-to-SQL - Database querying
• Bookshelf - Recommended reading
• Resources - Learning materials

Choose your path above and start building! Each section includes practical examples and production-ready code.