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Choosing the Right Embedding Dimension for Semantic Search
Executive Summary: Embedding dimension — the length of the vector used to represent text, images or other data — is a critical hyperparameter for semantic search. Higher dimensions let embeddings capt...
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Neuro-Symbolic AI; Explained Simply
Your model learns patterns beautifully. It just cannot explain why it made a call. That gap is exactly what neuro-symbolic AI was built to close. There is a running joke in enterprise ML. You spend s...
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Constrained Decoding: Forcing LLMs to Respect Your Taxonomy
A structural guarantee for LLM classification output Code: https://github.com/SachinKalsi/constrained-decoding The Problem When you use an LLM for classification across many categories, it sometimes ...
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