Word2Vec (Skip-Gram & CBOW)
Distributed Dense Word Representations via Continuous Vector Embeddings & Negative Sampling
#Embeddings#Skip-gram#CBOW#Distributed Representations#Negative Sampling
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STAGE 1 / 7— Anti-Pattern
Section 1: The LLM Anti-Pattern vs Right-Sized Model
The Naive Generative LLM Approach:
Calling an LLM API to fetch 1536-dimensional embeddings for 10 million single words in a vocabulary index.
Why It Fails in Production:
Huge API latency and costs; pre-trained Word2Vec or FastText vectors can be queried locally from RAM in nanoseconds.
Targeted Algorithm (Word2Vec (Skip-Gram & CBOW))
Latency:0.0001ms (RAM lookup)
Cost / 1M Ops:$0.00
Determinism:Exact Word Embedding
Generative LLM Alternative
Latency:500ms
Cost / 1M Ops:$2,000
Determinism:API Overhead & Delay