About the Course
About the Course
Modern AI applications require more than just semantic search. While vector embeddings allow systems to retrieve contextually similar information, they often fail when real-world constraints such as access control, document types, departments, or timestamps must be considered.
This course focuses on metadata filtering and schema-aware retrieval, a key technique used in production-grade RAG systems.
You will learn how to design metadata schemas, preserve metadata during document chunking, and apply filtering strategies during retrieval. These techniques allow AI systems to return precise, relevant, and policy-compliant answersinstead of relying purely on vector similarity.
Throughout the course, you will explore practical patterns such as:
Metadata-aware chunking
Query-time filtering
Role-based retrieval
Time-based constraints
Multi-filter search queries
By the end of the course, you will upgrade a basic RAG pipeline into a production-ready metadata-aware retrieval system.
Course Objectives
By the end of this course you will be able to:
Understand why vector similarity alone is insufficient for many RAG applications
Design metadata schemas that improve retrieval precision
Preserve metadata through chunking and ingestion pipelines
Implement role-based and time-based filtering during retrieval
Combine multiple metadata constraints for complex queries
Build and evaluate metadata-aware RAG workflows
Upgrade baseline RAG pipelines to production-ready systems
Prerequisites
Basic Python programming knowledge
Basic familiarity with Large Language Models or vector search
No prior experience with metadata filtering required
Your Instructor
Codersarts Team
Codersarts Team
