Phase 2: Embeddings & Semantic Search

Short summary

This tutorial explains embeddings and semantic search through a conversational dialogue, showing how tokenization converts text to token IDs and embedding layers create 1536-dimensional vectors that capture semantic meaning. Unlike keyword search, semantic search measures vector similarity to find conceptually related content even when words differ. Essential foundational knowledge for building RAG systems and AI products that understand meaning beyond exact keyword matches.

• •Tokenization converts text to numeric token IDs; embedding layers transform these into meaningful vectors with 1536 dimensions
• •Semantic search finds similar vectors rather than exact keywords, enabling 'team management' to match 'leadership experience'
• •Each vector dimension captures different aspects of meaning—frontend vs backend, syntax style, ecosystem size—enabling rich semantic understanding

Generated with AI, which can make mistakes.