Master the cutting edge of Artificial Intelligence by building real-world LLM applications with LangChain, Vector Databases, RAG systems, Fine-tuning, and Autonomous AI Agents with 100% placement support.
A structured journey from Transformer fundamentals to deploying production-ready AI Agents.
Every module is hands-on, focusing on real-world generative AI architectures and application building.
Introduction to Gen-AI, Self-Attention, Encoders/Decoders, and Tokenization.
Zero-shot, Few-shot, Chain-of-Thought (CoT), ReAct prompting, and guardrails.
Building with OpenAI GPT-4, Google Gemini, Anthropic Claude, and HuggingFace.
Chains, Memory, Document Loaders, Text Splitters, and Output Parsers.
Semantic search, dense vs sparse embeddings, ChromaDB, Pinecone, and FAISS.
Building enterprise RAG pipelines, Hybrid Search, Reranking, and Evaluation (Ragas).
Tool usage, Function calling, CrewAI, AutoGen, and LangGraph architectures.
PEFT, LoRA, QLoRA, fine-tuning Llama 3 / Mistral on custom datasets.
Image generation with Stable Diffusion, Midjourney API, and Multimodal LLMs.
Building and deploying a full-stack Gen-AI product using Streamlit, FastAPI, and Docker.
You'll emerge as an in-demand Generative AI Developer and LLM Applications Engineer.
Design optimal prompts, system instructions, and context windows for enterprise tasks.
Develop end-to-end AI applications leveraging state-of-the-art commercial and open-source models.
Build scalable Retrieval-Augmented Generation engines powered by vector databases.
Create autonomous multi-agent teams that execute complex workflows with external tools.
Adapt foundation models to specialized domain data using efficient LoRA techniques.
Build a robust portfolio of AI applications and launch your career in the Gen-AI domain.