Generative Ai With Context: Rag, Cag & Kag Applications
Published 12/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 8h 28m | Size: 6.17 GB
Master augmented generation in AI with RAG, CAG & KAG - covering semantic search, caching, knowledge graphs
What you'll learn
Build context‑aware AI pipelines using Retrieval Augmented Generation (RAG), Cache Augmented Generation (CAG), and Knowledge Augmented Generation (KAG).
Apply semantic search and embeddings to connect models with external knowledge sources for more accurate responses.
Optimize AI performance with caching strategies that reduce redundancy and improve efficiency in real‑world applications. - Leverage knowledge graphs for stru
Leverage knowledge graphs for structured reasoning, enabling AI systems to extract entities, facts, and relationships.
Debug and refine AI workflows step‑by‑step, gaining confidence in troubleshooting and improving pipeline reliability.
Translate theory into practice by completing hands‑on projects that demonstrate how augmented generation enhances generative AI.
Requirements
This course is designed to be accessible, even if you're new to context‑aware AI. There are no strict prerequisites, but having some basic familiarity with programming and machine learning will make the journey smoother.
Programming Skills: A beginner‑level understanding of Python is helpful, since all hands‑on projects are built in Python.
If you're a complete beginner, don't worry - the course is structured to guide you through installations, setup, and every workflow with clear explanations and exercises. The goal is to lower barriers and make advanced AI concepts approachable, so you can focus on learning and building.
Description
Artificial intelligence is evolving fast, and the next frontier is context. Models that can understand, retrieve, and reason with the right information at the right time are not only more accurate - they're smarter, more reliable, and closer to human‑like intelligence. This course, Context‑Aware AI, is designed to give you the skills and confidence to build those systems yourself.Instead of just theory, you'll get a guided journey through the most important context‑aware workflows: Retrieval Augmented Generation (RAG), Cache Augmented Generation (CAG), and Knowledge Augmented Generation (KAG). Each concept is introduced clearly, then reinforced with hands‑on projects that let you see the power of context in action. You'll learn how to connect models to external knowledge, optimize performance with caching, and integrate knowledge graphs for structured reasoning.What makes this course different is its balance of clarity and practicality. Complex ideas are broken down into intuitive explanations, supported by exercises and real‑world examples. By the end, you won't just understand how context‑aware AI works - you'll have built working pipelines that you can adapt to your own projects, whether you're developing smarter chatbots, enterprise search tools, or advanced reasoning systems.If you're ready to move beyond generic AI responses and start creating applications that truly understand context, this course is your launchpad. Join us and take the next step toward building intelligent systems that think deeper, respond smarter, and deliver real value.
Who this course is for
AI and Machine Learning practitioners
Data scientists and developers
Researchers and students
Tech professionals and innovators
Learners who want to move beyond basic AI concepts and start building intelligent systems that truly understand context
Generative Ai With Context: Rag, Cag & Kag Applications
