Generative AI and Agentic AI Course in Bangalore

Foundations. You begin with machine learning, deep learning and generative AI, how AI is used across industries, an introduction to large language models and AI ethics. Python for AI covers functions, object oriented programming, exception handling, file handling, NumPy, Pandas, Matplotlib and virtual environments. Natural language processing and deep learning fundamentals prepare you for transformers.

Working with language models. You study foundation models, the GPT architecture and BERT versus GPT, then prompt engineering from zero-shot and few-shot prompting to chain of thought, role-based prompts, prompt chaining, structured output and JSON responses. You learn how hallucinations happen and how to reduce them. With the OpenAI API you handle authentication, chat completions, function calling, streaming, rate limits, cost optimisation and error handling.

Giving models your own knowledge. In the RAG modules you load documents, chunk them, create embeddings and design retrieval pipelines with similarity, semantic and hybrid search. You store embeddings in FAISS, ChromaDB, Pinecone, Milvus and Weaviate, and build with LangChain and LlamaIndex, including chains, memory, output parsers, retrievers, query engines and chat engines.

Agentic AI. You learn what makes a system agentic: multi-step reasoning, planning and execution, memory, reflection and tool use. You build single and multi-agent systems, add human-in-the-loop checks, and compare LangGraph, CrewAI, AutoGen, the OpenAI Agents SDK, PydanticAI and Semantic Kernel. A full module covers the Model Context Protocol, including its architecture, clients and servers, tool registration and resource management.

Connecting agents to real work. You integrate web search, databases, REST APIs, SQL agents, file operations, email, calendars, PDFs and Excel, and build chatbots, document question answering systems, assistants and knowledge management tools.

Adapting and shipping. You study PEFT, LoRA, QLoRA, instruction tuning, evaluation and quantization. For deployment you use FastAPI and Flask, Docker, Kubernetes basics, cloud deployment, CI/CD, monitoring and logging.

Safety and governance. You cover AI risks, prompt injection, jailbreak prevention, data privacy, responsible AI, compliance and model monitoring.