Retrieval-Augmented Generation (RAG) is the cornerstone of production-ready AI systems that provide accurate, contextual responses grounded in real data. In this practical tutorial, you'll master the complete RAG pipeline: from data ingestion and vector embeddings to semantic search and context-aware response generation. Build a fully functional customer support chatbot that retrieves relevant knowledge base articles and generates contextual answers without hallucinations. Learn how enterprise teams deploy RAG systems at scale, understand the trade-offs between different vector databases, and discover best practices for optimising retrieval quality and response latency. Whether you're building internal knowledge systems, customer-facing chatbots, or document-based AI assistants, this course equips you with the practical skills and architectural knowledge to deploy RAG systems confidently in production environments.
Lessons
- Lesson 1: Understanding RAG and the Retrieval Pipeline — Core concepts, architecture, and why RAG solves the hallucination problem (+100 XP)
- Lesson 2: Text Embeddings and Vector Spaces — Converting text to embeddings and understanding semantic similarity (+100 XP)
- Lesson 3: Setting Up Your Vector Database — Evaluating and implementing vector storage with Pinecone or Chroma (+120 XP)
- Lesson 4: Building the Data Ingestion Pipeline — Chunking documents, generating embeddings, and indexing at scale (+120 XP)
- Lesson 5: Semantic Search and Context Retrieval — Implementing retrieval, ranking results, and handling edge cases (+100 XP)
- Lesson 6: Prompt Engineering for RAG Contexts — Crafting templates that leverage retrieved documents effectively (+100 XP)
- Lesson 7: Deploying and Monitoring Production RAG Systems — Building APIs, evaluating quality metrics, and optimising performance (+120 XP)
- Lesson 8: Building & Deploying Enterprise RAG Systems — Containerisation, multi-tenant considerations, latency optimisation, and real-world architectural trade-offs (+170 XP)