Prepare outlines

qdrant-landing/content/course/multi-vector-search/module-0/_index.md

@@ -1,6 +1,6 @@
 ---
 title: "Module 0: Setting Up Dependencies"
-description: Set up your development environment for multi-vector search. Install required dependencies and prepare your workspace for the course.
+description: "Set up your development environment for multi-vector search. Install required dependencies and prepare your workspace for the course."
 isLesson: true
 weight: 10
 ---
@@ -15,9 +15,7 @@ Get your environment ready for exploring multi-vector search with Qdrant.
 
 ## Today's path
 
-1. Python Environment Setup
-2. Installing Qdrant and FastEmbed
-3. Preparing Your Workspace
-4. Verifying Your Installation
+1. Qdrant Setup
+2. Installing Dependencies
 
 By the end, you'll have a working development environment ready for multi-vector search experiments.

qdrant-landing/content/course/multi-vector-search/module-1/_index.md

@@ -1,6 +1,6 @@
 ---
 title: "Module 1: Multi-Vector Representations for Textual Data"
-description: Learn about multi-vector representations for text with ColBERT. Understand how they differ from single vector embeddings and when to use them.
+description: "Learn about multi-vector representations for text with ColBERT. Understand how they differ from single vector embeddings and when to use them."
 isLesson: true
 weight: 20
 ---
@@ -15,9 +15,10 @@ Dive into multi-vector text representations and discover how ColBERT changes the
 
 ## Today's path
 
-1. ColBERT Basics
-2. Comparison to Regular Dense Embedding Models
+1. Late Interaction Basics
+2. MaxSim Distance Metric
 3. Use Cases for Multi-Vector Search
-4. MaxSim Distance Metric
+4. Problems of Multi-Vector Search
 
-You'll understand when multi-vector representations outperform traditional single-vector embeddings.
+You'll understand when multi-vector representations outperform traditional single-vector embeddings, and what kind of
+problems to expect when you start working with multi-vector search at scale.

qdrant-landing/content/course/multi-vector-search/module-2/_index.md

@@ -1,6 +1,6 @@
 ---
 title: "Module 2: Multi-Vector Representations for Multi-Modal Data"
-description: Explore multi-modal multi-vector search with ColPali. Learn how to search across images and text, and configure Qdrant for multi-vector embeddings.
+description: "Explore multi-modal multi-vector search with ColPali. Learn how to search across images and text, and configure Qdrant for multi-vector embeddings."
 isLesson: true
 weight: 30
 ---
@@ -15,9 +15,9 @@ Extend multi-vector representations beyond text to unlock powerful multi-modal s
 
 ## Today's path
 
-1. ColPali Family Overview
-2. How ColPali Models Work
+1. How ColPali Models Work
+2. ColPali Family Overview
 3. Visual Interpretability of ColPali
-4. Configuring Qdrant for Multi-Vector Embeddings
+4. Multi-Vector Embeddings in Qdrant
 
 You'll learn to build multi-modal search systems that understand both images and text.

qdrant-landing/content/course/multi-vector-search/module-3/_index.md

@@ -1,6 +1,6 @@
 ---
 title: "Module 3: Scalability and Optimization"
-description: Address scalability challenges in multi-vector search. Learn optimization techniques including quantization, pooling, MUVERA, and multi-stage retrieval.
+description: "Address scalability challenges in multi-vector search. Learn optimization techniques including quantization, pooling, MUVERA, and multi-stage retrieval."
 isLesson: true
 weight: 40
 ---
@@ -16,10 +16,10 @@ Tackle the memory and performance challenges of production-scale multi-vector se
 ## Today's path
 
 1. Memory Usage Implications and Solutions
-2. Vector Quantization Techniques (Scalar, Binary, +1.5/2-bit)
-3. Pooling Techniques (Row/Column, Hierarchical Token Pooling)
-4. MUVERA for HNSW Compatibility
+2. Vector Quantization Techniques
+3. Pooling Techniques
+4. MUVERA
 5. Multi-Stage Retrieval with Universal Query API
-6. Evaluating Search Pipelines (Cost vs. Latency)
+6. Evaluating Search Pipelines
 
 You'll master the optimization strategies needed to deploy multi-vector search at scale.