Build Generative AI applications using LangChain, mastering its key components.

Design multi-agent systems with CrewAI and LangChain tools, exploring their core elements in depth.

Create Retrieval-Augmented Generation (RAG) pipelines: input preparation, text chunking, embeddings, vector stores, similarity search, and pipeline integration.

Apply prompt engineering techniques with hands-on practice, including Basic, Role Task Context, Few-Shot, Chain of Thought, and Constrained Output Prompting.

Implement LangChain chains such as Single, Sequential, Math, RAG, Router, LLM Router, SQL Chains, and more.

Work with document loaders like CSVLoader, HTMLLoader, PDFLoader, and others.

Explore Hugging Face models and integrate them into practical Generative AI applications.

Master text chunking methods for RAG systems: Character, Recursive Character, Markdown Header, and Token Splitters.

Gain expertise in vector databases for RAG, including Pinecone, Chroma, Weaviate, Milvus, and FAISS.

Understand the fundamentals of AI, Machine Learning, Deep Learning, and Generative AI, along with their history and applications.

Learn how transformers work—attention mechanisms, encoding, and decoding.

Explore foundation models: their evolution, types, applications, and leading examples.

Compare language model performance, review top open-source LLMs, and learn how to select the right foundation model.

Embrace responsible AI practices, focusing on fairness, ethics, and bias mitigation.

Implement memory types in LLMs, including ConversationBufferMemory, Buffer Window, and ConversationSummaryMemory.