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Generative AI Developer Course

Master LLMs, ChatGPT, Azure OpenAI, LangChain, RAG pipelines, and AI-powered application development from scratch to production.

50 Hours
8 Modules
5+ Projects
✓ Certificate

Tools & Technologies Covered

🤖 ChatGPT / GPT-4o ☁ Azure OpenAI 🦜 LangChain 📚 LlamaIndex 🗃 Pinecone / Chroma 🐍 Python 🤗 Hugging Face 🖼 DALL-E / Stable Diffusion 🔷 Semantic Kernel ⚡ FastAPI 📓 Jupyter Notebooks 🏠 Ollama (Local LLMs)

📋 Detailed Course Syllabus

Module 1: Foundations of Generative AI 5 Hours
  • What is Generative AI? —Evolution and Landscape (2017–2025)
  • Discriminative vs. Generative Models —Key differences
  • Overview of Foundation Models: GPT, Claude, Gemini, LLaMA, Mistral
  • How LLMs work: Tokenization, Pre-training, RLHF, Instruction Tuning
  • Generative AI use cases across industries (Healthcare, Finance, Education, Retail)
  • Responsible AI —Ethics, Bias, Hallucination, Copyright concerns
Module 2: Prompt Engineering Mastery 6 Hours
  • Anatomy of a great prompt —Role, Context, Task, Format, Constraints
  • Zero-shot, One-shot, and Few-shot prompting techniques
  • Chain-of-Thought (CoT) and Tree-of-Thought (ToT) reasoning
  • ReAct pattern (Reasoning + Acting) for agentic tasks
  • Prompt templates and dynamic prompt construction
  • Advanced: Self-consistency, Meta-prompting, Prompt Chaining
  • Practical: OpenAI Playground and API prompt optimization lab
Module 3: OpenAI & Azure OpenAI APIs 7 Hours
  • OpenAI API setup —API keys, models, rate limits, pricing
  • Chat Completions API —system messages, temperature, max tokens
  • Function Calling and Structured Outputs (JSON mode)
  • Embeddings API —generating and using semantic embeddings
  • Azure OpenAI Service —deployment, endpoints, enterprise security
  • Whisper API (speech-to-text) and TTS API (text-to-speech)
  • DALL-E 3 API —image generation and editing
  • GPT-4 Vision (multimodal) —analyzing images with AI
  • Hands-on Project: Build a multi-modal AI assistant with the API
Module 4: LangChain Framework 8 Hours
  • LangChain architecture —chains, agents, memory, tools
  • LLM wrappers —OpenAI, Anthropic, Hugging Face, Azure integrations
  • Prompt templates, output parsers, and structured outputs
  • Document loaders —PDF, Word, web pages, Notion, databases
  • Text splitters and chunking strategies for large documents
  • LCEL (LangChain Expression Language) —composing chains fluently
  • LangChain Memory —ConversationBufferMemory, SummaryMemory
  • LangSmith —debugging, tracing, and monitoring LangChain apps
  • Project: Document Q&A chatbot with LangChain + PDF loaders
Module 5: Retrieval-Augmented Generation (RAG) 8 Hours
  • Why RAG? —Overcoming LLM knowledge cutoff and hallucination
  • RAG pipeline: Indexing →Retrieval →Generation
  • Vector databases deep dive: Pinecone, ChromaDB, FAISS, Weaviate, Qdrant
  • Embedding models: OpenAI ada-002, sentence-transformers, BGE models
  • Advanced RAG: HyDE, Query decomposition, Multi-query retrieval
  • Reranking with Cohere Rerank and cross-encoder models
  • RAG evaluation: Ragas framework —faithfulness, answer relevancy
  • LlamaIndex for complex RAG pipelines and document hierarchies
  • Project: Enterprise Knowledge Base Q&A system with RAG
Module 6: Fine-Tuning & Open Source LLMs 7 Hours
  • When to fine-tune vs. RAG vs. prompt engineering
  • Open source LLMs: LLaMA 3, Mistral, Phi-3, Gemma overview
  • Running LLMs locally with Ollama and LM Studio
  • Hugging Face Transformers —loading and using pre-trained models
  • LoRA and QLoRA fine-tuning —parameter-efficient training
  • Dataset preparation for fine-tuning —instruction format, quality
  • Fine-tuning with Unsloth, Axolotl, and Google Colab
  • Deploying fine-tuned models via Hugging Face Inference Endpoints
Module 7: Generative AI for Images & Multimodal 5 Hours
  • Image generation overview: DALL-E 3, Stable Diffusion, Midjourney
  • Stable Diffusion architecture —VAE, U-Net, CLIP text encoder
  • ControlNet for guided image generation
  • Running Stable Diffusion locally with Automatic1111
  • GPT-4 Vision —image analysis and understanding
  • Building multimodal apps: combine text + image inputs and outputs
  • Video generation overview: Sora, RunwayML, Pika
Module 8: Production Deployment & Capstone Project 4 Hours
  • Building production-ready AI APIs with FastAPI + LangChain
  • Streaming responses (Server-Sent Events) for chat UIs
  • Cost optimization: prompt caching, model selection, batching
  • Security: prompt injection defense, input/output validation
  • Monitoring AI applications: LangSmith, Azure AI Studio logging
  • Deployment: Azure App Service, Docker containers, Hugging Face Spaces
  • Capstone Project: Full-stack AI SaaS application (chatbot + document Q&A)

What You'll Be Able to Build After This Course

Custom AI Chatbots integrated with your business data and branded UI
Document Q&A Systems that answer questions from PDFs, Word docs, and databases
Semantic Search Engines using vector databases for intelligent document retrieval
AI-Powered APIs that expose LLM capabilities to web and mobile apps
Image Generation Apps using DALL-E and Stable Diffusion integrations
Production AI Applications deployed on Azure with monitoring and security

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