Generative AI Course with Real‑World Projects

Complete Generative AI Course Curriculum

Our curriculum is structured to take you from beginner to expert level. Each module builds upon the previous one, ensuring comprehensive understanding of generative AI technologies.

• 1

  Foundations of LLMs & Generative AI

  ▼

  Local dev setup, First OpenAI API call, and “Hello AI” project to generate creative text responses

  • Introduction to Large Language Models
  • Setting up development environment
  • First OpenAI API integration
  • Building “Hello AI” project
  • Creative text generation basics
• 2

  AI Agents & Agentic Workflows

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  Build simple LangChain chains and create decision-making agents for domain-specific queries

  • Understanding AI agents
  • LangChain framework basics
  • Building decision-making workflows
  • Domain-specific agent development
  • Agent communication patterns
• 3

  Building Basic Chat Applications

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  Develop chatbots with short-term memory using LangChain and vector databases like Qdrant Cloud

  • Chatbot architecture design
  • Short-term memory implementation
  • Vector database integration
  • Qdrant Cloud setup
  • Conversation flow management
• 4

  Chat Over Large Documents

  ▼

  Create “Ask my PDF” bots using Qdrant DB, Pinecone, and LangChain for document-based conversations

  • Document processing techniques
  • PDF parsing and indexing
  • Vector similarity search
  • Pinecone integration
  • Document-based Q&A systems
• 5

  Retrieval-Augmented Generation (RAG)

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  Build working RAG pipelines and create RAG-powered news summarizers with vector stores

  • RAG architecture fundamentals
  • Vector store implementation
  • Retrieval strategies
  • News summarization systems
  • RAG pipeline optimization
• 6

  Memory-Aware AI & Graph DB

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  Implement AI with persistent long-term memory using Neo4j and entity extraction with graph visualization

  • Long-term memory systems
  • Neo4j graph database
  • Entity extraction techniques
  • Graph visualization
  • Persistent memory patterns
• 7

  Multi-Modal LLM Applications

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  Build simple multi-modal apps and create image-based Q&A bots using OpenAI Vision API

  • Multi-modal AI concepts
  • OpenAI Vision API
  • Image processing for AI
  • Visual Q&A systems
  • Cross-modal understanding
• 8

  Security, Guardrails & MCP

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  Implement guardrails pipelines and build sensitive data redaction tools with security best practices

  • AI security fundamentals
  • Guardrails implementation
  • Data redaction techniques
  • Security best practices
  • Model Control Protocol (MCP)
• 9

  LangGraph & Checkpointing

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  Create LangGraph workflows with retries and build multi-agent research report generators

  • LangGraph framework
  • Workflow design patterns
  • Retry mechanisms
  • Multi-agent systems
  • Research automation
• 10

  Human-in-the-Loop & Tool Binding

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  Develop AI systems with human approval steps and create email drafting AI with confirmation workflows

  • Human-AI collaboration
  • Approval workflows
  • Tool binding techniques
  • Email automation systems
  • Confirmation mechanisms
• 11

  Model Fine-Tuning & LLM-as-Judge

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  Fine-tune small models and build resume scoring systems using OpenAI fine-tuning and Perplexity API

  • Model fine-tuning techniques
  • OpenAI fine-tuning API
  • LLM-as-Judge patterns
  • Resume scoring systems
  • Perplexity API integration
• 12

  Deployment & Final Projects

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  Deploy fully functional AI apps using AWS, Docker, and Langfuse with capstone project presentations

  • AWS deployment strategies
  • Docker containerization
  • Langfuse monitoring
  • Production deployment
  • Capstone project development