1. Understanding Generative AI
Grasp the core concepts of Generative AI, its principles, and real-world uses, highlighting its differences from traditional AI.
2. Interacting with LLMs
Master effective interactions with Large Language Models (LLMs), including Retrieval Augmented Generation (RAG) and the use of contextual data for enhanced AI responses.
3. Vector Databases for Knowledge Retrieval
Learn to utilize Vector Databases for semantic search and knowledge retrieval, essential for creating intelligent, context-aware AI systems.

1. Building Context-Aware AI Systems

Enhance your skills in combining LLMs with VectorDBs and RAG pipelines to create intelligent applications that reason over large datasets and deliver meaningful outputs.

2. Exploring Multimodal Intelligence

Understand and implement AI systems that process and combine multiple data types—text, audio, image, and video—bridging the gap between different modalities.

3. Applying Semantic and Similarity Search

Learn the principles of semantic understanding and similarity-based search to enable AI applications that can interpret, categorize, and retrieve information intuitively.

1. Designing Intelligent Agentic Workflows

Master the creation of autonomous, multi-agent systems using frameworks like Agno, CrewAI, and LangGraph. Learn how agents collaborate to execute complex workflows efficiently.

2. Integrating AI with Modern MCP Ecosystems

Gain hands-on expertise in integrating AI with tools such as Playwright MCP, Mongo MCP, and GitHub MCP to automate data handling, testing, and development operations.

3. Building Real-World AI Applications

Apply end-to-end AI pipelines to case studies like credit card fraud detection, loan automation, and video analysis—bridging theory with practical enterprise-grade implementation.