Develop a practical understanding of Retrieval-Augmented Generation (RAG)

Design user-friendly, interactive interfaces for RAG applications using Gradio

Learn about LlamaIndex, its uses in building RAG applications, and how it contrasts with LangChain

Build RAG applications using LangChain and LlamaIndex in Python

Differentiate between vector databases and traditional databases based on their functionality and use cases

Execute fundamental database operations in ChromaDB, including updating, deleting, and managing collections

Understand and apply similarity search techniques, both manually and with ChromaDB, and develop recommendation systems using these techniques

Develop a thorough and comprehensive understanding of key internal mechanisms within RAG

Differentiate between various retrieval patterns and assess their effectiveness in RAG applications 

Implement advanced retrievers and FAISS to optimize information retrieval and similarity search 

Design a comprehensive RAG application by integrating LangChain, FAISS, and a front-end UI using Gradio

Evaluate retrieval strategies and refine AI-driven search capabilities for improved performance

Build the job-ready skills you need to build multimodal generative AI applications in just 3 weeks

Understand the fundamental concepts and challenges in multimodal AI, including the integration of text, speech, images, and video

Build multimodal AI applications using state-of-the-art models and frameworks such as IBM’s Granite, Meta’s Llama, OpenAI’s Whisper, DALL·E and Sora

Develop multimodal AI solutions, including chatbots and image/video generation models, using IBM watsonx.ai, Hugging Face, Flask and Gradio

Develop AI agents that can reason and perform tasks independently

Implement tool calling and chaining to create structured AI workflows

Utilize built-in LangChain agents to analyze data, generate visualizations, and execute database queries

Apply best practices in prompt engineering and tool calling to enhance AI agent performance

Build agentic AI systems using LangChain and LangGraph to support memory, iteration, and conditional logic

Design and implement self-improving agents using Reflection, Reflexion, and ReAct architectures

Apply agent orchestration techniques to build collaborative multi-agent systems

Implement agentic RAG systems that route queries and support retrieval-enhanced reasoning

Analyze foundational AI design patterns to evaluate their effectiveness in structuring scalable AI workflows 

Construct multi-agent systems using CrewAI by configuring agents and tasks 

Develop AI-driven applications using BeeAI and AutoGen

Design optimized AI solutions by combining appropriate frameworks and design patterns