About the Course
The Schema-Aware Retrieval for RAG Systems course is designed for developers and AI engineers who want to build production-grade retrieval systems capable of handling real-world data complexity.
Most Retrieval-Augmented Generation (RAG) pipelines assume that all data can be treated as plain text. This assumption breaks down immediately when users ask structured queries like:
👉 “Show me all high-priority billing issues from last month”
Traditional vector search cannot:
Apply filters like date ranges
Understand structured attributes
Distinguish between categorical fields and text
This course solves that problem by teaching you how to build schema-aware retrieval systems — systems that understand your data structure before performing semantic search.
You will learn how to:
Analyze and interpret schemas from different data sources
Design chunking strategies that preserve structured metadata
Build multiple types of indexes (hash, inverted, range, vector)
Parse natural language queries into structured filters
Combine structured filtering with semantic retrieval
Design fallback strategies for edge cases
By the end of the course, you will build a complete query engine from scratch that:
Handles both structured and unstructured queries
Applies filters accurately
Performs semantic search when needed
Degrades gracefully when data is incomplete
This course focuses on real-world system design, making it highly valuable for production AI applications.
What You Will Learn
Why vector search fails for structured queries
How to extract and understand schemas from real data sources
Schema-aware chunking strategies for better embeddings
Building multi-index retrieval systems
Query parsing using regex and LLM-based approaches
Hybrid retrieval strategies combining filters + embeddings
Designing fallback mechanisms for edge cases
Evaluating retrieval systems using precision and recall
Tools & Technologies
Python
Jupyter Notebook / Google Colab
Vector Embeddings
BM25 (Lexical Search)
Claude API (for query parsing)
Multi-index Retrieval Systems
JSON, SQL, CSV Data Processing
Who Should Enroll
RAG developers facing limitations with vector search
AI engineers building data-driven applications
Backend developers working with structured databases
Data engineers integrating AI with real-world systems
Developers building enterprise AI search systems
Anyone serious about production-level AI engineering
Prerequisites
Strong Python fundamentals
Basic understanding of embeddings and cosine similarity
Familiarity with JSON and structured data
Optional: basic SQL knowledge
Real-World Use Cases
Enterprise knowledge systems with filters
AI-powered analytics dashboards
Customer support systems with structured data
Financial and billing data retrieval systems
Legal and compliance document search
SaaS platforms with complex query handling
Your Instructor
Codersarts Team
