What will you learn?
Build GenAI apps using LangChain and agents with memory, tools, and prompt chaining.
Create AI chatbots and assistants using GPT, OpenAI APIs, and RAG with vector search.
Fine-tune LLMs using Hugging Face, LoRA, and PEFT for custom content generation.
Master core GenAI concepts like foundation models, embeddings, and prompt engineering.
Course Content for Generative AI Mastery
- What is Generative AI?
- Evolution from traditional AI to generative models
- Key capabilities: Text, Image, Audio, Video generation
- Examples: ChatGPT, DALL-E, Midjourney
- Limitations and risks of Gen AI models
- Understanding foundation models (GPT, PaLM, Claude)
- Fine-tuning vs pre-training
- Prompt engineering basics
- Tokenization and embeddings in Gen AI
- Role of temperature, top-k, and top-p sampling
- Use cases across industries (healthcare, finance, education)
- Introduction to LangChain framework
- Building chains for structured LLM applications
- Prompt templates: why and how to use them
- Memory in LangChain: ConversationBufferMemory, BufferWindowMemory
- Chaining multiple LLM calls and tools together
- Agents in LangChain
- What is a vector database?
- Storing embeddings for fast similarity search
- Introduction to popular Vector DBs: Pinecone, FAISS, Chroma, Weaviate
- Indexing and retrieval techniques
- Integrating vector search into RAG pipelines
- Understanding RAG (Retrieval-Augmented Generation)
- Setting up a dataset of medical FAQs, health guidelines, and insurance policies
- Building a vector store of healthcare documents and knowledge base
- Querying the vector DB and combining retrieved results with an LLM to generate accurate answers
- Deploying an assistant to handle patient queries like symptoms, treatment options, insurance coverage
- Evaluating assistant performance: accuracy, response time, patient satisfaction
- Understanding user intents and FAQs for e-commerce
- Designing conversation flows
- Integrating product catalog search with the bot
- Building the bot using LangChain and OpenAI APIs
- Deploying chatbot to a website or messaging platform
- Handling errors and fallback responses
- What are AI agents?
- Differences between agents and standalone LLMs
- How agents use tools and memories
- Simple examples of tool-using agents
- Introduction to ReAct (Reasoning + Acting) pattern
- Multi-agent collaboration and communication
- Agent architectures: Supervisor-Agent patterns, Planning Agents
- Dynamic tool use and task decomposition
- Evaluating agent performance
- Real-world applications of agentic workflows (travel planning, coding assistants)
- What is fine-tuning in LLMs?
- Dataset preparation for fine-tuning
- Low-Rank Adaptation (LoRA) and Parameter-Efficient Fine-Tuning (PEFT)
- Fine-tuning using Hugging Face Transformers
- Evaluating and deploying fine-tuned models
- Bias and fairness issues in generative models
- Data privacy concerns with Gen AI
- Risks of misinformation and deepfakes
- Responsible AI principles
- Regulatory landscape and compliance (GDPR, AI Act)
Requirements
Everything You Need to Get Started:Basic Python programming knowledge is required to write scripts and use libraries like NumPy and Pandas.
Prior understanding of Machine Learning algorithms such as regression, classification, and clustering is essential.
Math concepts like linear algebra, probability, and basic calculus help in learning deep learning models.
Strong interest in Deep Learning and NLP is important to stay motivated and complete real-world projects.
Meet your instructor
Mr. Hemant Sethi
AI Expert | 20+ Years of Experience in ML, DL, NLP & Generative AIHemant is an experienced AI professional specializing in Machine Learning, Deep Learning, Natural Language Processing, and Generative AI. With a passion for teaching, he makes complex AI topics simple and practical. Hemant empowers learners to build real-world AI solutions and succeed in the fast-growing field of artificial intelligence.
Buy for 10% off
This course include:
35+ Hours of Live Generative AI Classes with hands-on training.
Project-Based Learning using real-world GenAI use cases.
Live Doubt-Solving Support during every session.
Job-Focused Interview Prep for Generative AI and LLM roles.
Chat Now
