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AI Syllabus Module
Module 1.7: Embeddings
Convert textual characters into high-dimensional vectors to measure similarities mathematically.
Lessons & Submodules
What are Embeddings?
Understand how text maps to high-dimensional coordinate spaces.
Vector Similarity
Compare Cosine, Dot Product, and L2 distance metrics.
Cosine Similarity
Deep dive into Cosine Similarity calculations and normalization.
Chunking for Embeddings
Analyze fixed-size, recursive, and semantic document chunking.
Embedding Model Tradeoffs
Compare dimensions sizes, context limits, and cost profiles.
Resume/JD Matching Architecture
Design ATS pipelines that match profiles using similarity scores.
Embeddings Interview Guide
Prepare for technical interviews focused on vector calculations.
Key Skills
- •Calculate Cosine similarity scores between document vectors
- •Explain the dimension sizes of standard embedding models
Interview Value
- Why does cosine similarity fail to represent semantic matches when document lengths differ heavily?