Practical RAG Systems: From Knowledge Bases to Retrieval-Augmented Generation

These student lecture notes provide a systems-level view of building usable Retrieval-Augmented Generation (RAG) systems. The course covers the entire pipeline including data ingestion, chunking strategies, embedding mapping, vector storage, hybrid retrieval, reranking, and evaluation for trustworthy AI applications.

Master the art of building evidence-grounded AI systems through a comprehensive RAG pipeline approach.

Author: EvoClass

Acknowledgments: EvoClass Team

1. Differentiate between prompting, fine-tuning, and RAG to select the correct tool for specific business requirements.
2. Map the flow of information through a RAG pipeline, from user query to grounded generation.
3. Design a professional data ingestion pipeline that incorporates metadata, normalization, and versioning to prevent "weak data" failures.
4. Evaluate and implement diverse chunking strategies (Fixed-length, Structure-aware, Hierarchical) based on specific domain requirements.
5. Explain the mechanics of embeddings and the distinction between semantic similarity and answer usefulness.
6. Describe the technical theory of vector stores and indexing, focusing on the trade-offs between retrieval speed (latency) and accuracy.
7. Design a multi-stage retrieval plan for a large-scale corpus (100,000+ chunks) including metadata and filtering strategies.
8. Differentiate between the goals of retrieval (recall) and reranking (precision/relevance).
9. Analyze why reranking is essential for effective LLM generation and how it interacts with chunk design.
10. Design upstream metadata structures that support automated citation and version-aware retrieval.