Module 25: Generative AI & Modern LLM Applications

Build LLM apps with prompt engineering, vector search, RAG, and agents. Treat demos as demos until you add eval and monitoring.

How this module fits: Stage 7 in the main README. Best after Module 12 (NLP) or alongside it if you learn by building products. This folder is a hub: generative-ai-llms.md maps topics; the real depth is in resources/generative_ai_comprehensive_guide.md, resources/rag_comprehensive_guide.md, and the linked guides below. Use the project tutorial and quick reference here for hands-on practice.

Suggested path: Prompt engineering → embeddings and vector search → RAG pipeline → agents → production checklist in resources/genai_production_deployment.md.

What You'll Learn

• Prompt Engineering (Zero-shot, Few-shot, Chain-of-Thought)
• Vector Databases (Pinecone, ChromaDB, Weaviate, FAISS)
• RAG (Retrieval-Augmented Generation) Systems
• LLM Agents (LangChain, LangGraph, AutoGPT)
• Multi-Agent Systems (CrewAI, AutoGen)
• Building Production GenAI Apps
• Generative Configuration Parameters
• Model Evaluation and Benchmarks

Topics Covered

1. Prompt Engineering

• What is Prompt Engineering: Designing effective inputs for LLMs
• Zero-shot Prompting: No examples, rely on pre-trained knowledge
• Few-shot Prompting: Provide examples to guide behavior
• Chain-of-Thought: Step-by-step reasoning
• Generative Configuration: Temperature, top-p, top-k, repetition penalty
• Unsupported answers: Mitigation strategies (RAG helps grounding; it does not fully eliminate errors)
• Text Embeddings: Vector representations for semantic search
• Advanced Techniques: Role-playing, output formatting, constraints

2. Vector Databases

• What are Vector Databases: Storage for high-dimensional embeddings
• Pinecone: Managed cloud vector database
• ChromaDB: Open-source, Python-first vector database
• Weaviate: GraphQL-based vector search engine
• FAISS: Facebook AI Similarity Search library
• Semantic Search: Finding similar documents by meaning
• Similarity Metrics: Cosine similarity, Euclidean distance
• Choosing the Right Database: Comparison and use cases

3. RAG (Retrieval-Augmented Generation)

• RAG Architecture: Retrieval + Augmentation + Generation
• Document Ingestion: Loading and processing documents
• Text Chunking: Strategies for splitting documents
• Embedding Generation: Creating vector representations
• Vector Database Integration: Storing and retrieving embeddings
• Query Processing: User query to embedding conversion
• Context Augmentation: Combining retrieved context with prompts
• LLM Integration: Generating responses with augmented context
• Evaluation Metrics: RAG-specific evaluation methods
• Production Patterns: Deployment and optimization strategies

4. LLM Agents

• What are AI Agents: Autonomous systems that perceive, reason, and act
• LangChain Agents: Building agents with LangChain
• LangGraph: Graph-based agent workflows
• AutoGPT: Fully autonomous goal completion
• Tools and Function Calling: Integrating external tools
• Memory and Context: Managing conversation history
• ReAct Framework: Reasoning and Acting for tool use
• PAL (Program-aided Language Models): Code generation for precise problem solving
• Agent Evaluation: Measuring agent performance

5. Multi-Agent Systems

• Multi-Agent Architectures: Coordinated agent workflows
• Agent Coordination: Communication and task distribution
• Specialized Roles: Planner, Research, Writer agents
• CrewAI: Framework for role-playing agents
• AutoGen: Conversational multi-agent systems
• MCP (Model Context Protocol): Standardized context sharing
• A2A Communication: Agent-to-agent protocols

6. Building Production GenAI Apps

• Tech Stack: Frontend, backend, LLM frameworks, vector databases
• Streamlit: Fast Python-based UI for GenAI apps
• FastAPI: Building GenAI backends
• Deployment Strategies: Cloud, on-premise, hybrid
• Cost Optimization: Reducing API and infrastructure costs
• Monitoring and Observability: Tracking performance and usage
• Security Best Practices: API keys, input validation, rate limiting
• Generative AI Project Lifecycle: From problem definition to maintenance

Learning Objectives

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

• Design effective prompts for various LLM tasks
• Set up and use vector databases for semantic search
• Build end-to-end RAG systems for document Q&A
• Create autonomous AI agents with tool integration
• Design and implement multi-agent systems
• Deploy GenAI applications to production
• Optimize LLM applications for cost and performance
• Evaluate and benchmark LLM applications

Projects

1. RAG System for Document QA: Build a system that ingests PDFs, stores embeddings, and answers questions
2. LLM-Powered Research Agent: Create an autonomous agent that researches topics and generates reports
3. Multi-Agent Content Creation: Build a system with specialized agents (Planner, Research, Writer)
4. Prompt Engineering Playground: Experiment with different prompting techniques
5. Vector Database Comparison: Compare different vector databases for your use case

Key Concepts

• Prompt Engineering: The art and science of communicating with LLMs
• Semantic Search: Finding information by meaning, not keywords
• RAG: Combining retrieval with generation for knowledge-augmented AI
• Agents: Autonomous systems that can reason and use tools
• Vector Embeddings: Dense representations capturing semantic meaning
• Generative Configuration: Parameters controlling LLM output (temperature, top-p, etc.)
• Production Deployment: Making GenAI apps reliable, scalable, and cost-effective

Documentation & Learning Resources

Official Documentation:

• LangChain Documentation: Complete LangChain guide
• LangGraph Documentation: Graph-based workflows
• Pinecone Documentation: Managed vector database
• ChromaDB Documentation: Open-source vector database
• Hugging Face Transformers: Pre-trained models
• OpenAI API Documentation: GPT models and API

Free Courses:

• LangChain Crash Course: Free YouTube course
• RAG Tutorial (LangChain): Free tutorial
• Vector Databases Course: DeepLearning.AI course
• Building LLM Applications: DeepLearning.AI course

Tutorials:

• Prompt Engineering Guide: Prompt patterns and failure modes
• RAG Implementation Guide: RAG pipeline notes
• LangChain Tutorial: LangChain framework guide
• AI Agents Guide: Building AI agents
• Vector Databases Guide: Vector database comparison

Video Tutorials:

• LangChain Crash Course (YouTube)
• RAG Tutorial (YouTube)
• Building LLM Apps (YouTube)
• Vector Databases Explained

Practice:

• LangChain Templates: Example projects
• RAG Examples: RAG implementations
• Hugging Face Spaces: Deploy and share GenAI apps
• LangChain Playground: Experiment with LangChain

Complete Detailed Guide →

Additional Resources:

• Advanced Topics →: Advanced RAG, agent architectures, optimization
• Project Tutorial →: Step-by-step RAG system project
• Quick Reference →: Quick lookup guide for GenAI
• Generative AI Guide: Concept overview
• RAG Guide: Deeper RAG implementation notes
• LangChain Guide: LangChain framework details
• AI Agents Guide: AI agents and multi-agent systems
• GenAI Production Deployment: Production deployment patterns

Previous Module: 12-natural-language-processing
Next (recommended stage order): 20-handling-imbalanced-data (Stage 7.5) → then 13-model-deployment (Stage 8)

Folder order note: Module 19 (SQL) is Stage 1.5, study it in parallel with Modules 01–02, not after this module. See FOUNDATION_AND_JOB_READINESS.md.