Retrieval-Augmented Generation (RAG) Model Certification Program
- Understand RAG architecture and retrieval pipelines
- Integrate LLMs with vector databases and embeddings
- Build knowledge-grounded AI assistants for real-world use cases
- Reduce hallucinations and improve response accuracy in GenAI systems
Bonus Resources
Included
Vector Database Workflow Templates
Access structured indexing pipelines, embedding strategies, retrieval configuration examples, and semantic search workflow blueprints used in modern enterprise RAG systems.
Embeddings & Chunking Strategy Playbooks
Learn document ingestion workflows, chunk-size optimization techniques, metadata-aware indexing strategies, and context-window tuning approaches to improve retrieval relevance.
RAG Evaluation & Hallucination Reduction Toolkit
Practice evaluation techniques for measuring retrieval accuracy, grounded-response quality, answer relevance scoring, and hallucination mitigation strategies.
Enterprise Knowledge Assistant Project Pack
Build portfolio-ready knowledge assistants using retrieval pipelines, semantic search workflows, and production-style architecture examples aligned with real-world GenAI assistant implementations.
Learning Outcomes
By the end of this Retrieval-Augmented Generation (RAG) program, you will gain the practical skills required to design retrieval pipelines, integrate vector databases with LLM systems, and deploy production-ready knowledge assistants that deliver accurate, context-aware responses.
Understand RAG System Architecture
Learn how retrievers, embeddings, vector databases, and generators work together to create reliable knowledge-grounded AI assistant pipelines.
Build Semantic Retrieval Pipelines
Design retrieval workflows using embeddings and similarity search to deliver context-aware responses from structured knowledge sources.
Implement Document Chunking Strategies
Apply chunk-size optimization, metadata indexing strategies, and document ingestion pipelines to improve retrieval accuracy.
Reduce Hallucinations in LLM Responses
Use retrieval grounding techniques and prompt augmentation workflows to improve answer reliability across enterprise AI assistant systems.
Evaluate Retrieval Performance Effectively
Measure answer relevance, retrieval precision, and context quality using structured evaluation workflows designed for production RAG systems.
Deploy Enterprise Knowledge Assistants
Build portfolio-ready knowledge assistants using retrieval pipelines, vector indexing workflows, and scalable deployment architectures.
Retrieval-Augmented Generation (RAG) Model Certification Program
Live Instructor-Led Cohort Training
Training Breakdown
A structured learning journey into building Retrieval-Augmented Generation (RAG) systems using embeddings, vector databases, semantic retrieval pipelines, and enterprise knowledge assistant architectures.
Session 1 (4 Hours): Foundations of Retrieval-Augmented Generation
- Understanding limitations of standalone LLM responses
- Introduction to Retrieval-Augmented Generation architecture
- Retriever + generator workflow overview
- Enterprise knowledge assistant use cases
- Real-world RAG implementation scenarios
Session 2 (4 Hours): Embeddings & Vector Database Integration
- Embeddings and semantic similarity concepts
- Vector search fundamentals
- Indexing strategies for retrieval pipelines
- Working with FAISS / Pinecone / ChromaDB concepts
- Optimizing retrieval performance
Session 3 (4 Hours): Document Processing & Chunking Pipelines
- Document ingestion workflow design
- Chunk-size optimization strategies
- Metadata-aware indexing approaches
- Handling structured vs unstructured data sources
- Improving retrieval relevance across large datasets
Session 4 (4 Hours): Building End-to-End RAG Applications
- Retriever–generator workflow integration
- Prompt augmentation strategies
- Multi-source knowledge retrieval pipelines
- Reducing hallucinations using grounded context
- Designing production-style assistant pipelines
Session 5 (4 Hours): Deployment & Enterprise Knowledge Assistants
- Deploying scalable RAG systems
- Monitoring retrieval quality and response accuracy
- Evaluation workflows for RAG pipelines
- Security and governance considerations
- Building portfolio-ready enterprise assistant projects
Meet Your Instructor
Prof. Sagar Zilpe
Certified PRINCE2 Agile Trainer
With 20+ years of experience in AI, Agile, and project delivery, Sagar brings real-world project expertise to every session, making complex frameworks simple, practical, and actionable for enterprise environments.
Get Certified By WorKnoW Academy!
Yes! You will get a course completion certificate once you complete the course and the assignment!
Refund Policy
48-Hours from first session100% Risk-Free Guarantee
FAQ
Does this include Peoplecert certification cost?
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Include your certification in resume and also we have provision to validate all certificates rolled out to ensure the employers get to hear best practices you out through the learning journey.
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You may contact our team via email [email protected] or call +91 9607979893, more country support lines to get added.