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Expert Review: Retrieval Optimization: Tokenization to Vector Quantization

By Prosun • December 21, 2025

Course Overview

This course, provided by DeepLearning.AI, is characterized by its rigor and practical application focus. The curriculum covers essential concepts: In Retrieval Optimization: Tokenization to Vector Quantization, taught by Kacper Łukawski, Developer Relations Lead of Qdrant, you’ll all about tokenization and also how to optimize vector search in your largescale customerfacing RAG applications. You’ll explore the technical details of how vector search works and how to optimize it for better performance. focuses on optimizing the first step in your RAG and search results. You’ll see how different tokenization techniques like BytePair Encoding, WordPiece, and Unigram work and how they affect search relevancy. You’ll also how to address common challenges such as terminology mismatches and truncated chunks in embedding models. To optimize your search, you need to be able to measure its quality. several quality metrics for this purpose. Most vector databases use Hierarchical Navigable Small Worlds (HNSW) for approximate nearestneighbor search. You’ll see how to balance the HNSW parameters for higher speed and maximum relevance. Finally, you would use different vector quantization techniques to enhance memory usage and search speed. What you’ll do, in detail: about the internal workings of the embedding model and how your text is turned into vectors. Understand how several tokenizers such as BytePair Encoding, WordPiece, Unigram, and SentencePiece are trained. Explore common challenges with tokenizers such as unknown tokens, domainspecific identifiers, and numerical values, that can negatively affect your vector search. Understand how to measure the quality of your search across several quality metrics. Understand how the main parameters in HNSW algorithms affect the relevance and speed of vector search and how to optimally adjust these parameters. Experiment with the three major quantization methods, product, scalar, and binary, and how they impact memory requirements, search quality, and speed. By the end of , you’ll have a solid understanding of how tokenization is done and how to optimize vector search in your RAG systems.