Integrating Knowledge Bases for RAGs (2026)

Use LlamaIndex and Qdrant to build RAG-ready knowledge bases. Ingest documents, generate embeddings, optimize retrieval, and maintain accurate, scalable AI search across enterprise content.
What you'll learn
Enterprise teams face a growing challenge: how to extract meaningful answers from sprawling, siloed data sources. In this course, Integrating Knowledge Bases for RAGs, you'll gain the ability to build retrieval-augmented generation systems (RAGs) that deliver accurate, context-aware responses grounded in enterprise knowledge. First, you'll explore how knowledge bases unify structured and unstructured data, enabling semantic search through chunking, metadata, and indexing strategies. Next, you'll discover how large language models (LLMs) interact with vector databases like Qdrant using embeddings and retrieval configurations to optimize performance. Finally, you'll learn how to build and evaluate a practical RAG pipeline using LlamaIndex, applying real-world techniques for preprocessing, filtering, and ranking retrieved results. When you're finished with this course, you'll have the skills and knowledge of enterprise-ready RAG systems needed to transform static documentation into intelligent, conversational tools.
Homepage