Generative AI & Modern LLM Applications

This file is the module hub (map + links), not a full textbook chapter. Deep implementation lives under resources/. Work the suggested path below, then return here for the next topic.

Map of modern LLM application topics: prompt engineering, vector databases, and RAG. Deep dives live under resources/.

Table of Contents

• Introduction
• Prompt Engineering
• Vector Databases
• RAG Systems
• LLM Agents
• Multi-Agent Systems
• Production Deployment
• Resources

Introduction

Why This Module?

The AI industry has rapidly shifted from training models from scratch to using pre-trained foundational models (LLMs). This module teaches you how to:

• Use LLMs effectively through prompt engineering
• Build RAG systems for knowledge-augmented AI
• Create AI agents that can reason and use tools
• Deploy production GenAI apps that solve real problems

What You'll Build

By the end of this module, you'll be able to:

• Build RAG systems for document Q&A
• Create autonomous AI agents
• Deploy GenAI applications to production
• Optimize LLM applications for cost and performance

Prompt Engineering

For comprehensive prompt engineering coverage, see:

• Generative AI Comprehensive Guide - Prompt Engineering Section

Key Topics:

• Zero-shot and few-shot prompting
• Chain-of-thought reasoning
• Handling hallucinations
• Text embeddings
• Advanced techniques

Vector Databases

For detailed vector database coverage, see:

• Generative AI Comprehensive Guide - Vector Databases Section

Key Topics:

• Pinecone, ChromaDB, Weaviate, FAISS
• Semantic search
• Similarity matching
• Embedding storage and retrieval

RAG Systems

For comprehensive RAG implementation, see:

• RAG Comprehensive Guide

Key Topics:

• RAG architecture
• Document processing
• Embedding generation
• Vector database integration
• Evaluation metrics

LLM Agents

For detailed agent development, see:

• AI Agents Guide

Key Topics:

• LangChain agents
• LangGraph workflows
• Tool integration
• Memory management
• Agent evaluation

Multi-Agent Systems

For multi-agent system development, see:

• AI Agents Guide - Multi-Agent Systems

Key Topics:

• Agent coordination
• CrewAI and AutoGen
• MCP (Model Context Protocol)
• Specialized agent roles

Production Deployment

For production deployment patterns, see:

• GenAI Production Deployment Guide

Key Topics:

• Deployment strategies
• Cost optimization
• Monitoring and observability
• Security best practices

Resources

Comprehensive Guides

• Generative AI Guide: Overview of core concepts
• RAG Guide: RAG implementation notes
• LangChain Guide: LangChain framework
• AI Agents Guide: AI agents
• GenAI Production Deployment Guide: Production patterns

Quick Start

1. Start with Prompt Engineering
2. Learn Vector Databases
3. Build a RAG System
4. Create LLM Agents
5. Deploy to Production

Next: Complete the Advanced Project "LLM Chatbot & RAG System" to apply these concepts.