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RAG: Retrieval-Augmented Generation in Practice

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A premium, complete RAG (Retrieval-Augmented Generation) course, updated for 2026. Covers RAG fundamentals, embeddings and vector representations, chunking strategies, end-to-end RAG pipelines, vector databases (Pinecone, Qdrant, pgvector, Weaviate), semantic and hybrid search, re-ranking, multimodal RAG, evaluation metrics (RAGAS, DeepEval), 2026 embedding models, document processing, advanced architectures (Agentic RAG, Self-RAG, CRAG, GraphRAG, conversational RAG), production scaling, caching, security, frameworks (LangChain, LlamaIndex, Haystack), pipeline orchestration, CI/CD testing, monitoring, case studies and a complete hands-on project.

11 modules
27 lessons
~24h duration
v1.0 version
AI professor An AI agent built into every lesson — ask questions and get instant answers based on the course content
Hands-on exercises Real scenarios and practical exercises directly on the platform, with instant feedback
Progress & analytics A personal dashboard with statistics, streaks, scores and structured learning paths
Interactive AI quizzes Questions generated by AI and adapted to your level, with detailed explanations
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Content in English
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What you will learn

Practical skills you gain by completing this course

RAG Fundamentals
Implementing the RAG Pipeline
Embedding Models and Document Processing
Advanced RAG
Advanced RAG Architectures
RAG in Production
RAG Tools and Frameworks
Systematic RAG Evaluation and Testing
Case Studies and Hands-On Projects
Final Quiz — RAG in Practice
Appendix: Official Resources, 2026 Updates and Learning Paths

Who it is for

Developers Software engineers Solution architects CTOs / Tech Leads Data Scientists ML Engineers DevOps Engineers

Recommended level

Advanced

Assumes hands-on experience with AI and complex scenarios.

Updates

Regular

Content updated regularly with the latest practices from the industry.

Category

IT & Engineering

A technical course for IT professionals — available with individual course access or the IT Pro / All Access bundle.

Advanced level

Hands-on experience required

Assumes practical experience with AI. Covers complex scenarios and advanced strategies.

Always up to date

Up-to-date content

The course is updated regularly with the latest information, tools and practices from the industry.

Practical and applied

27 lessons with real examples

Each lesson includes practical scenarios, actionable checklists and quizzes to check your understanding.

Curriculum

11 modules, 27 lessons — structured to learn step by step.

11 modules
27 lessons
~24h of content
Interactive quizzes
Free preview available What Is RAG and Why It Matters
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1 Free preview lesson What Is RAG and Why It Matters
Read the preview
2 Embeddings and Vector Representations
55 min
3 Chunking Strategies: The Art of Document Fragmentation
55 min
1 Building an End-to-End RAG Pipeline
55 min
2 Vector Databases in Practice
55 min
3 Semantic Search vs Keyword Search
55 min
1 Embedding Models: Comparison and Selection 2026
55 min
2 Document Processing: PDF, HTML, Office, and Complex Structures
55 min
3 Multimodal Embeddings: Images, Tables, and Audio
55 min
1 Hybrid Search and Re-ranking
55 min
2 Multi-Modal RAG: Images, Tables, and Charts
55 min
3 RAG Evaluation Metrics
55 min
1 Agentic RAG, Self-RAG, and Corrective RAG
55 min
2 RAG with Knowledge Graphs
55 min
3 Conversational RAG: Memory and Multi-Turn Context
55 min
1 Scaling RAG Systems
55 min
2 Caching and RAG Optimization
55 min
3 Security and Data Governance in RAG Systems
55 min
1 LangChain, LlamaIndex, and Haystack: Complete 2026 Comparison
55 min
2 Orchestrating RAG Pipelines in Production
55 min
1 Systematic RAG Evaluation with RAGAS and DeepEval
55 min
2 CI/CD Testing and RAG Monitoring in Production
55 min
1 Case Study: RAG System for Technical Documentation
55 min
2 Hands-On Project: Build a Complete RAG System
60 min
1 Final Assessment — RAG: Retrieval-Augmented Generation
60 min
1 Official Resources, 2026 Updates and Learning Paths
32 min
2 Contextual Retrieval: Enriching Chunks before Embedding
24 min
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