Name: RAG Course | Production RAG Training - 20 Hours in 1.5 Months
Price: 20000 INR
Availability: InStock

Question 1

What will I learn in this RAG course?

Accepted Answer

You’ll learn how to design and ship production RAG systems end-to-end: document ingestion, chunking, embeddings, vector databases, semantic + hybrid retrieval, evaluation and monitoring, and secure deployment for enterprise knowledge bases. Advanced modules include Graph RAG, agentic workflows, and multimodal retrieval.

Question 2

Who should take this RAG course?

Accepted Answer

This RAG course is designed for AI/ML engineers building LLM applications, backend developers implementing intelligent search, data engineers working with knowledge systems, and developers who want to specialize in production AI infrastructure. It's ideal for engineers who need to reduce LLM hallucinations and build enterprise knowledge AI systems.

Question 3

Do I need prior machine learning or deep learning knowledge for this RAG course?

Accepted Answer

Basic understanding of Python and APIs is required, but deep machine learning expertise is not necessary. The course focuses on RAG system architecture, vector database engineering, and production deployment rather than model training. Familiarity with LLM APIs (like OpenAI or Anthropic) is helpful but not required.

Question 4

What vector databases will I learn in this RAG course?

Accepted Answer

You'll gain hands-on experience with production vector databases including Pinecone, Weaviate, ChromaDB, Qdrant, and Milvus. You'll learn vector indexing strategies, similarity search algorithms, hybrid search architecture, and how to choose the right vector database for different production RAG use cases.

Question 5

What is the difference between basic RAG and advanced RAG techniques like Graph RAG and Agentic RAG?

Accepted Answer

Basic RAG retrieves documents using vector similarity search. Graph RAG integrates knowledge graphs for complex reasoning and relationship-aware retrieval. Agentic RAG uses AI agents to orchestrate multi-step retrieval, validation, and synthesis. This course covers all three: foundational RAG pipelines, Graph RAG for structured knowledge, and Agentic RAG for autonomous retrieval workflows.

Question 6

What kind of RAG projects will I build during the course?

Accepted Answer

You'll build enterprise-grade RAG applications including: (1) Enterprise knowledge chatbot with hybrid search, (2) Secure internal documentation Q&A system, (3) Graph RAG system for complex knowledge bases, (4) Agentic RAG workflow with multi-step retrieval, and (5) Production RAG pipeline with evaluation and monitoring. All projects reflect real-world enterprise use cases.

Question 7

How is this RAG course different from LangChain or LlamaIndex tutorials?

Accepted Answer

Unlike framework-specific tutorials, this course teaches production RAG engineering principles—chunking strategies, embedding model selection, vector database design, hybrid retrieval architecture, evaluation frameworks, and enterprise security. You'll use LangChain and LlamaIndex as tools, but the focus is on building reliable, scalable, monitored RAG systems that work in real production environments.

Question 8

What is hybrid retrieval and why is it important for RAG systems?

Accepted Answer

Hybrid retrieval combines dense vector search (semantic similarity) with sparse retrieval methods like BM25 (keyword matching). This approach improves RAG system accuracy by capturing both semantic meaning and exact term matches. Production RAG systems often use hybrid retrieval to balance precision and recall, especially for domain-specific or technical queries.

Question 9

How do I evaluate and monitor RAG systems in production?

Accepted Answer

The course teaches RAG evaluation frameworks including faithfulness metrics (does the answer match retrieved context?), relevance scoring (is retrieved context useful?), context precision, and answer quality. You'll implement observability tools to trace retrieval latency, monitor embedding quality, detect retrieval failures, and measure end-to-end RAG system performance in production.

Question 10

What is Graph RAG and when should I use it?

Accepted Answer

Graph RAG combines knowledge graphs with vector retrieval for relationship-aware question answering. It's ideal for complex knowledge bases with entities and relationships—like organizational hierarchies, technical documentation with dependencies, or medical knowledge. Graph RAG enables multi-hop reasoning and contextual retrieval that pure vector search cannot achieve.

Question 11

Is this RAG course suitable for building secure enterprise knowledge AI systems?

Accepted Answer

Yes. The course includes dedicated modules on enterprise RAG security covering private data handling, access control, compliance requirements (GDPR, HIPAA), data residency, secure vector database deployment, and strategies for deploying RAG systems with sensitive internal knowledge while maintaining LLM safety and privacy.

Question 12

What embedding models will I learn to use for RAG systems?

Accepted Answer

You'll work with production embedding models including OpenAI embeddings (text-embedding-3-small/large), Cohere embeddings, open-source Sentence Transformers, and domain-specific fine-tuned embeddings. You'll learn embedding model selection criteria, dimensionality trade-offs, cost optimization, and how to fine-tune embeddings for specialized domains.

Question 13

How long does the RAG course take and what is the time commitment?

Accepted Answer

The course is designed as a 1.5-month program with approximately 20 hours of guided training, plus optional practice time for assignments and projects. It’s structured to work well for working professionals.

Question 14

Will I get a RAG certification after completing the course?

Accepted Answer

Yes. Upon successful completion of all projects and assignments, you'll receive a Production RAG Engineer Certificate from School of Core AI. This credential demonstrates expertise in building enterprise-grade Retrieval Augmented Generation systems, vector database engineering, and production knowledge AI deployment.

RAG Course –Master Production Retrieval Systems

Start Your RAG Journey

How RAG Works in Production

Documents

Embeddings

Vector DB

Retrieval

Grounded LLM Response

AI/ML Engineers Building LLM Apps

Backend Engineers Integrating Search

Data Engineers Working With Knowledge Systems

AI Product Engineers

Developers Transitioning to AI Infrastructure

Production RAG Skills You'll Build

Vector Search & Retrieval Pipelines

Hybrid & Advanced RAG Architecture

Production Deployment & Evaluation

RAG Engineering Curriculum & Roadmap

Production RAG Foundations

Phase 1Introduction to Retrieval Augmented Generation

Phase 2Embedding Models and Semantic Representation

Phase 3Chunking Strategies for RAG

Vector Databases & Semantic Search

Phase 1Vector Database Architecture

Phase 2Vector Search Implementation

Phase 3Search Quality Optimization

Hybrid Retrieval & Advanced Search

Phase 1Sparse Retrieval Methods

Phase 2Hybrid Search Architecture

Graph RAG & Knowledge Graphs

Phase 1Introduction to Graph RAG

Phase 2Graph-Based Retrieval

Multimodal RAG

Phase 1Beyond Text Retrieval

Phase 2Advanced Multimodal Use Cases

Agentic RAG & Orchestration

Phase 1Introduction to Agentic RAG

Phase 2Implementing Agentic RAG

RAG with LangChain & LlamaIndex

Phase 1LangChain for RAG

Phase 2LlamaIndex for RAG

RAG Evaluation & Monitoring

Phase 1RAG Evaluation Frameworks

Phase 2Testing and Regression Detection

Phase 3Observability and Monitoring

Enterprise RAG Security & Compliance

Phase 1Secure RAG Architecture

Phase 2Compliance and Governance

Production RAG Deployment

Phase 1RAG System Architecture

Phase 2Deployment Strategies

Phase 3Performance Optimization

Capstone: Enterprise RAG Projects

Phase 1Real-World RAG Applications

Get the RAG Course Brochure

Free Course Brochure

Production RAG Stack You’ll Master

Vector Databases

Embedding Models

RAG Frameworks

Agentic RAG & Orchestration

Search & Reranking

Knowledge Graphs

Evaluation & Monitoring

Deployment & Security

Enterprise Knowledge Chatbot with Hybrid Search

Secure Internal Documentation RAG with Access Control

Graph RAG System for Complex Knowledge Bases

Agentic RAG Research Assistant with Multi-Step Retrieval

Industry-Recognized Certificate

Course Fees & Enrollment

What You’ll Get

RAG Course FAQs