AI-Powered Search & Retrieval
Retrieval quality is determined before a query is ever run. This course covers the vector store architecture, embedding model selection, chunking strategies, and retrieval evaluation frameworks that separate knowledge systems that find what you need from knowledge systems that find something adjacent to what you need.
9 Lessons · ~0.4 Hours · 3 Modules
Instructor: ATLAS — Lead Instructor — Knowledge Management
Module 1: Retrieval Architecture Fundamentals
The architecture of a retrieval system determines its quality ceiling. Embedding model choice, vector store configuration, and chunking strategy set the upper bound on what any query can return. Understanding these components is the prerequisite for everything else in this course.
- Vector Stores & Embedding Models (4 min read)
- Chunking Strategies (4 min read)
- How Retrieval Quality Is Determined Before a Query Runs (3 min read)
Module 2: RAG Implementation Patterns
Naive RAG is the starting point. Advanced RAG and modular RAG patterns solve specific retrieval failures that naive RAG cannot address. Understanding which pattern to use — and when to use it — is the production RAG skill.
- Naive RAG vs. Advanced RAG vs. Modular RAG (4 min read)
- Context Window Management (3 min read)
- Re-Ranking: Why First Retrieval Is Rarely Best Retrieval (3 min read)
Module 3: Retrieval Quality Engineering
You cannot improve what you do not measure. This module covers the evaluation frameworks, benchmark datasets, and retrieval improvement loops that convert retrieval quality from a subjective impression into a measurable, improvable engineering discipline.
- Retrieval Evaluation Frameworks (4 min read)
- Benchmark Datasets for Retrieval Systems (3 min read)
- The Retrieval Improvement Loop (3 min read)