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Posted: 2025-04-13 17:38:14 UTC
This article contains some claims that remain unverified. While much of the content may be accurate, exercise care when relying on this information.
This article contains some claims that remain unverified. While much of the content may be accurate, exercise care when relying on this information.
Status
Last Updated
2025-04-13 17:38:34 UTC
Verified By
Rollup News
This course teaches how tokenization works and how to optimize vector search in Retrieval Augmented Generation (RAG) systems, focusing on improving retrieval quality, speed, and memory.
Understanding the internal workings of embedding models.
Learning how tokenizers like Byte-Pair Encoding, WordPiece, Unigram, and SentencePiece work.
Exploring challenges with tokenizers and their impact on vector search.
Measuring search quality across relevance, ranking, and score-related metrics.
Understanding how HNSW parameters affect vector search and how to tune them.
Experimenting with quantization methods to optimize memory, search quality, and speed.
Unknown tokens
Domain-specific identifiers
Numerical values negatively affecting vector search