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Retrieval Augmented Generation: Building Context-Aware AI Systems

7 chapters · 32 lessons

1. 1. RAG Architecture and Core Components

   4 lessons

   Understand the retrieval augmented generation pattern and how it solves hallucination and knowledge currency problems.

2. 2. Document Processing and Embedding Generation

   5 lessons

   Transform unstructured documents into searchable vector representations with effective chunking and metadata strategies.

3. 3. Vector Database Implementation

   5 lessons

   Set up and optimize vector databases for fast, accurate semantic search in production environments.

4. 4. Prompt Construction and Context Integration

   4 lessons

   Build effective prompts that combine retrieved documents with user queries to produce grounded, accurate responses.

5. 5. Evaluation and Quality Assurance

   5 lessons

   Measure and improve RAG system accuracy using retrieval metrics, answer quality scoring, and failure analysis.

6. 6. Production Deployment and Optimization

   5 lessons

   Deploy RAG systems with latency optimization, caching, security controls, and monitoring for real-world use.

7. 7. Advanced Patterns and Multi-Step Retrieval

   4 lessons

   Extend RAG with query rewriting, multi-hop retrieval, and agent-based workflows for complex knowledge tasks.