2. What skills will I gain from a RAG course?

You can develop skills in document preparation, text chunking, embeddings, vector search, retrieval pipelines, grounded generation, and retrieval optimization. The course also covers hybrid search, reranking, query expansion, RAG evaluation, security, and performance improvement.

3. What is Retrieval-Augmented Generation used for?

RAG can connect language models to external documents and knowledge sources so applications can retrieve relevant information before generating responses. Common applications include document question answering, knowledge assistants, research tools, customer support systems, and enterprise information systems.

4. Does the course cover vector databases and embeddings?

Yes. The curriculum covers text embeddings, embedding models, vector databases, similarity search, metadata filtering, and retrieval optimization. Learners explore how these components contribute to effective RAG pipelines.

5. What projects are included in the RAG course?

Projects include a document Q&A system, knowledge base assistant, research assistant, customer support RAG system, and complete RAG pipeline. Learners also work on testing, improving, and deploying a production-ready RAG application.

RAG Course: Build Retrieval-Augmented Generation Applications

Category Data Analytics & AI

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A RAG course teaches how Retrieval-Augmented Generation combines information retrieval with large language models to produce responses grounded in external knowledge. This course introduces the architecture, data preparation, retrieval methods, and application development techniques needed to create reliable RAG systems.

Learners explore document preprocessing, text chunking, metadata, embeddings, vector databases, similarity search, retrieval pipelines, grounded generation, hybrid search, reranking, query expansion, and multi-query retrieval. The course also covers RAG APIs, knowledge assistants, document question answering, AI agent integration, and conversation context management.

Evaluation, security, and optimization are central to the curriculum, covering retrieval quality, response accuracy, hallucination reduction, sensitive document protection, and retrieval-based attacks. Through structured projects, learners build document Q&A systems, research assistants, customer support applications, and production-ready RAG pipelines.

RAG architecture, retrieval processes, and LLM integration
Data preparation, document processing, chunking, and metadata
Embeddings, vector databases, and similarity search
RAG pipelines, grounded responses, and retrieval failure handling
Advanced retrieval, hybrid search, reranking, and query expansion
RAG application development, evaluation, security, and optimization

Basic familiarity with artificial intelligence, large language models, or software concepts is helpful but not mandatory. Some programming or API experience can be beneficial for learners working with RAG application development and integrations.

This course is designed for AI developers, software engineers, data professionals, ML practitioners, developers, and technology professionals interested in knowledge-based AI applications. It is also suitable for learners who want to build document assistants, research systems, customer support tools, and AI applications using external knowledge.

1.1 Understanding Retrieval-Augmented Generation
1.2 Why RAG Matters
1.3 RAG Versus Traditional LLM Applications
1.4 RAG System Architecture
1.5 Retrieval and Generation Processes
1.6 RAG Capabilities and Limitations

2.1 Identifying RAG Data Sources
2.2 Collecting and Organizing Documents
2.3 Document Cleaning and Preprocessing
2.4 Text Chunking Strategies
2.5 Managing Metadata
2.6 Preparing Documents for Retrieval

3.1 Understanding Text Embeddings
3.2 Creating Document Embeddings
3.3 Choosing Embedding Models
3.4 Understanding Vector Databases
3.5 Performing Similarity Search
3.6 Optimizing Retrieval Results

4.1 Designing a RAG Pipeline
4.2 Document Ingestion Workflows
4.3 Implementing Document Retrieval
4.4 Connecting LLMs to Retrieved Context
4.5 Generating Grounded Responses
4.6 Handling Retrieval Failures

5.1 Improving Chunking Strategies
5.2 Metadata-Based Filtering
5.3 Hybrid Search Techniques
5.4 Reranking Retrieved Results
5.5 Query Expansion Techniques
5.6 Multi-Query Retrieval

6.1 Building a RAG Application
6.2 Working with RAG APIs
6.3 Creating Knowledge Assistants
6.4 Building Document Question Answering
6.5 Integrating RAG with AI Agents
6.6 Managing Conversation Context

7.1 Evaluating Retrieval Quality
7.2 Measuring Response Accuracy
7.3 Reducing RAG Hallucinations
7.4 Protecting Sensitive Documents
7.5 Preventing Retrieval-Based Attacks
7.6 Optimizing RAG Performance

8.1 Building a Document Q&A System
8.2 Creating a Knowledge Base Assistant
8.3 Developing a Research Assistant
8.4 Building a Customer Support RAG System
8.5 Testing and Improving a RAG Pipeline
8.6 Deploying a Production-Ready RAG Application

A RAG course is suitable for developers, AI engineers, software professionals, ML practitioners, data professionals, and technology teams building generative AI applications. Learners with basic knowledge of LLMs can benefit particularly from the application-focused curriculum.

RAG Course: Build Retrieval-Augmented Generation Applications

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