add seo

qdrant-landing/content/blog/qdrant-deeplearning-ai-course.md

@@ -17,13 +17,13 @@ tags:
   - Vector Database
 ---
 
-We’re excited to announce a new course on DeepLearning.AI's platform: [Retrieval Optimization: From Tokenization to Vector Quantization](https://www.deeplearning.ai/short-courses/retrieval-optimization-from-tokenization-to-vector-quantization/?utm_campaign=qdrant-launch&utm_medium=qdrant&utm_source=partner-promo). This collaboration between Qdrant and DeepLearning.AI aims to empower developers and data enthusiasts with the skills needed to enhance search capabilities in their applications.
+We’re excited to announce a new course on DeepLearning.AI's platform: [Retrieval Optimization: From Tokenization to Vector Quantization](https://www.deeplearning.ai/short-courses/retrieval-optimization-from-tokenization-to-vector-quantization/?utm_campaign=qdrant-launch&utm_medium=qdrant&utm_source=partner-promo). This collaboration between Qdrant and DeepLearning.AI aims to empower developers and data enthusiasts with the skills needed to enhance [vector search](/advanced-search/) capabilities in their applications.
 
 Led by Qdrant’s Kacper Łukawski, this free, one-hour course is designed for beginners eager to delve into the world of retrieval optimization.
 
 ## Why This Collaboration Matters
 
-At Qdrant, we believe in the power of effective search to transform user experiences. Partnering with DeepLearning.AI allows us to combine our cutting-edge vector search technology with their educational expertise, providing learners with a comprehensive understanding of how to build and optimize Retrieval-Augmented Generation (RAG) applications. This course is part of our commitment to equip the community with practical skills that leverage advanced machine learning techniques.
+At Qdrant, we believe in the power of effective search to transform user experiences. Partnering with DeepLearning.AI allows us to combine our cutting-edge vector search technology with their educational expertise, providing learners with a comprehensive understanding of how to build and optimize [Retrieval-Augmented Generation (RAG)](/rag/rag-evaluation-guide/) applications. This course is part of our commitment to equip the community with practical skills that leverage advanced machine learning techniques.
 
 <iframe width="560" height="315" src="https://www.youtube.com/embed/AE8i69Kcodc?si=IdTEKlUHVbGzgJD-" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
 
@@ -33,7 +33,7 @@ In this course, you’ll explore key concepts that will enhance your understandi
 
 - Learn how tokenization works in large language and embedding models and how the tokenizer can affect the quality of your search.
 - Explore how different tokenization techniques including Byte-Pair Encoding, WordPiece, and Unigram are trained and work.
-- Understand how to measure the quality of your retrieval and how to optimize your search by adjusting HNSW parameters and vector quantizations.
+- Understand how to [measure the quality of your retrieval](/rag/rag-evaluation-guide/) and how to optimize your search by adjusting HNSW parameters and [vector quantizations](/articles/what-is-vector-quantization/).
 
 ## Who Should Enroll