Course Includes:
- Price: FREE
- Enrolled: 0 students
- Language: English
- Certificate: Yes
- Difficulty: Advanced
Coupon verified 04:19 AM (updated every 10 min)
Course Folder
What You'll Learn
- Design and build production-ready RAG architectures for real-world and enterprise AI applications.,Implement advanced retrieval techniques including Hybrid Search
- BM25
- semantic search
- and cross-encoder re-ranking.,Build and deploy Graph RAG systems using knowledge graphs
- entity extraction
- and relationship mapping.,Develop Agentic RAG and Multi-Agent AI systems capable of planning
- reasoning
- and autonomous retrieval.,Create Multi-Modal RAG applications that can retrieve and understand PDFs
- images
- audio
- video
- and structured data.,Apply advanced retrieval strategies such as semantic chunking
- parent-child retrieval
- HyDE
- and context compression.,Evaluate RAG systems using industry-standard metrics including precision
- recall
- faithfulness
- context relevance
- and latency benchmarks.,Optimize and scale AI systems using vector databases
- caching
- performance tuning
- and monitoring best practices.
Requirements
- Basic familiarity with Python programming is recommended but not required. The course includes step-by-step explanations for all major concepts and implementations.,A fundamental understanding of AI
- machine learning
- or large language models (LLMs) will be helpful
- but beginners with a strong technical interest can follow along.,A computer (Windows
- macOS
- or Linux) with internet access.,An interest in building real-world AI applications and experimenting with modern AI tools and frameworks.,No prior experience with Retrieval-Augmented Generation (RAG)
- Graph RAG
- or Agentic AI is required.
Description
“This course contains the use of artificial intelligence”
The demand for intelligent AI applications has exploded, but most Retrieval-Augmented Generation (RAG) systems fail when deployed in production. Basic implementations often suffer from poor retrieval quality, hallucinations, limited context understanding, and scalability challenges. This course is designed to take you beyond introductory RAG concepts and teach you how to build production-ready RAG systems used in modern enterprises.
In this comprehensive masterclass, you will learn how to design and implement advanced retrieval architectures, including Corrective RAG (CRAG), Self-RAG, Agentic RAG, and Adaptive RAG. You'll explore how leading organizations build reliable AI applications by combining large language models (LLMs) with intelligent retrieval pipelines, robust evaluation frameworks, and scalable infrastructure.
We begin by understanding why traditional RAG implementations fail and how to architect modern solutions from the ground up. You will master advanced techniques such as semantic chunking, parent-child retrieval, sliding window strategies, and context preservation to improve retrieval accuracy and response quality. From there, you'll dive into Hybrid Search, combining dense vector retrieval with BM25, and learn how to optimize results using cross-encoder re-ranking, query expansion, and Hypothetical Document Embeddings (HyDE).
Next, you'll build next-generation systems using Graph RAG, enabling AI applications to reason over knowledge graphs, entities, and relationships. You'll also explore Agentic RAG and Multi-Agent Systems, where AI agents collaborate, plan, invoke tools, and autonomously retrieve information to solve complex tasks. In addition, you'll learn how to develop Multi-Modal RAG applications capable of understanding PDFs, images, audio, video, and structured datasets within a unified knowledge platform.
Production deployment is a major focus of this course. You'll learn how to evaluate RAG systems using industry-standard metrics such as precision, recall, faithfulness, context relevance, and latency benchmarks. We'll also cover distributed vector databases, caching strategies, performance tuning, monitoring, security, and governance to ensure your applications are ready for enterprise environments.
Throughout the course, you'll complete five hands-on projects, including building a Hybrid Search RAG system, implementing Graph RAG with Knowledge Graphs, creating an Agentic AI Assistant, developing a Multi-Modal PDF and Image RAG application, and evaluating a production pipeline using real-world techniques.
By the end of this course, you will have the skills and confidence to design, build, evaluate, scale, and deploy advanced AI systems powered by modern RAG architectures. Whether you're a software engineer, AI developer, machine learning practitioner, data scientist, or technical leader, this course will provide the practical knowledge needed to build the next generation of enterprise AI applications.
Who this course is for:
- This course is designed for software engineers
- AI engineers
- machine learning practitioners
- and developers who want to build production-ready AI applications using advanced Retrieval-Augmented Generation (RAG) techniques.,It is ideal for data scientists and technical professionals looking to expand their expertise in Hybrid Search
- Graph RAG
- Agentic AI
- and Multi-Modal Retrieval. If youre currently working with large language models and want to improve reliability
- scalability
- and performance in enterprise environments
- this course will provide the practical skills you need.,The course is also an excellent fit for solution architects
- engineering managers
- and technology leaders who want to understand how modern organizations design and deploy intelligent knowledge systems.,Whether youre an experienced developer seeking to master next-generation AI architectures or a motivated learner looking to transition into the rapidly growing field of AI engineering
- this course will equip you with the knowledge and hands-on experience needed to build
- evaluate
- and deploy advanced RAG systems in the real world.
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