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Generative AI & Enterprise RAG Systems 2026
4.8
Offered by DeepLearning.AI & IBMSpecializationAccredited
Generative AI & Enterprise RAG Systems 2026
Architect production retrieval-augmented generation: semantic vector indexing, hybrid BM25 search, context re-ranking, and self-correcting RAG loops.
4.8(28,450 ratings)
•95,000 already enrolled
Instructor: Dr. Alexander Ross
Chair of Software Engineering · DeepLearning.AI & IBM
Curriculum Overview
About This Course
Architect production retrieval-augmented generation: semantic vector indexing, hybrid BM25 search, context re-ranking, and self-correcting RAG loops.
Throughout this track, you will transition from core conceptual mental models to building hardened, real-world software. Every module combines structured conceptual explanations with live in-browser coding katas, architectural diagrams, and automated test assertions. By completing the hands-on milestones, you build a verified portfolio demonstrating deep competency to engineering leaders and top employers.
Interactive In-Browser Katas
Write, compile, and debug real code in our integrated sandbox with instant test verification and syntax hints.
Production Architecture
Explore real enterprise patterns, memory trade-offs, concurrency paradigms, and scalable system design.
Verifiable Certificate
Earn an accredited, shareable digital credential authenticated by DeepLearning.AI & IBM and ASCI Institute.
Portfolio Capstone Project
Build and deploy an end-to-end applied capstone deliverable ready to showcase on your GitHub and resume.
Curriculum Highlights & Architectural Focus
- Chunking Protocols: Recursive Character, Markdown Header & Semantic Splitting
- Hybrid Retrieval: Dense Vector Embeddings combined with Sparse BM25 Keywords
- Cross-Encoder Reranking for Precision Filtering (Cohere, BGE-Reranker)
- The RAG Triad: Groundedness, Context Relevance, and Answer Quality Evals
- Corrective RAG (CRAG) & Self-RAG Agentic Fallback Pipelines
Technologies, Frameworks & Tooling Covered
PineconeWeaviateLlamaIndexLangChainCohere RerankOllamaUnstructured.io
Course Specifications
Offered ByDeepLearning.AI & IBM
CredentialSpecialization
Skill LevelAdvanced
Curriculum4 Modules · 4 Lessons
Commitment10 Weeks · Self-Paced
InstructionEnglish · Auto Subtitles & Transcripts
CertificateIncluded upon Completion
Free Audit Available · Full Access Included with Plus
Who Should Take This Course?
Ideal for students, software engineers, and technical professionals aiming to master Generative AI & Enterprise RAG Systems 2026, pass technical interviews, and build production-grade applications.
Official Partner Masterclass
Official Course Video & Keynote
Official Course Video:DeepLearning.AI & IBM
Click to Watch Official Lecture
1080p HDGenerative AI & Enterprise RAG Systems 2026
Official Lecture & WalkthroughKey Lecture Chapters & Timestamps
What you will learn
Key competencies verified by DeepLearning.AI & IBM
Master production-grade engineering principles in Generative AI & Enterprise RAG Systems 2026
Write clean, idiomatic, and highly maintainable code conforming to industry standards
Solve rigorous real-world problem sets with automated in-browser feedback
Build a portfolio-grade capstone project ready for technical interviews and resume presentation
Skills you will gain
Software EngineeringAlgorithmsClean CodeSystems ArchitectureProduction Deployment
Shareable Certificate
Earn a certificate upon completion backed by DeepLearning.AI & IBM.
100% Online & Self-Paced
Start instantly and learn at your own schedule from any device.
Hands-on Challenge Katas
Write, compile, and debug real code in the browser IDE.
Flexible Deadlines
Reset deadlines according to your personal availability.
Syllabus: What is in this Specialization
4 Modules · 4 Lessons · 4 Capstone Projects
•
1
Module 1: Document Ingestion, Chunking & Embeddings
Parse PDFs, code repositories, and markdown tables into semantically coherent chunk vectors.
1.1
1.1 Chunk Overlap & Token Budget Preservation
Configure sliding window text chunking with 15% token overlap to preserve cross-boundary semantics.
Coding KataLaunch
2
Module 2: Hybrid Retrieval (Dense Vector + BM25)
Combine vector semantic search with sparse keyword matching using Reciprocal Rank Fusion (RRF).
3
Module 3: Cross-Encoder Reranking & Context Compression
Filter top 50 retrieved chunks down to top 5 high-signal chunks using deep cross-encoders.
4
Module 4: RAG Triad Evals & Self-Corrective CRAG
Automate groundedness checks and trigger web search fallbacks when confidence drops.
Capstone Applied Learning Project
Generative AI & Enterprise RAG Systems 2026 Industry Capstone
Architect and deploy an end-to-end production application demonstrating all core competencies acquired throughout the Generative AI & Enterprise RAG Systems 2026 curriculum.
Final Deliverable
Complete open-source repository with documentation, unit test suite, and deployment configuration.
Technologies & Stack
GitVS CodeTerminalDocker
Instructors
Learn from industry pioneers and senior educators from DeepLearning.AI & IBM
Dr. Alexander Ross
Chair of Software Engineering
DeepLearning.AI & IBM
Senior educator and systems architect with over 15 years of industry and academic experience.
91%
Positive Career Outcome
of graduates reported significant skill advancement and improved readiness for engineering roles.
Median reported compensation: ₹26,00,000 / year
Learner Reviews
4.8 out of 5(28,450 ratings)
January 2026
"Clear explanations, excellent hands-on challenge katas, and great pacing. Highly recommended."
Cameron ReedSoftware Developer at Atlassian
Verified LearnerFrequently Asked Questions
Answers to common queries about certificates, grading, and prerequisites