Back to AI DashboardModule 5: Embeddings and Vector Databases
AI Engineer Track
Module 5: Embeddings and Vector Databases
Master vector representation spacing, similarity indexing algorithms, and hybrid metadata keyword searches.
Syllabus Modules
Vector SpacesComing Soon
Understand dense vector projections mapping semantical relations.
Total Lessons: 0Explore Module
Similarity MetricsComing Soon
Compare Cosine, Dot Product, and L2 distance metrics characteristics.
Total Lessons: 0Explore Module
Indexing StrategiesComing Soon
Study Hierarchical Navigable Small World (HNSW) graphs and IVF indexes.
Total Lessons: 0Explore Module
Chunking StrategiesComing Soon
Map fixed-size, sentence-recursive, and parent-child document splits.
Total Lessons: 0Explore Module
Hybrid SearchComing Soon
Blend keyword index checks with dense semantic matches.
Total Lessons: 0Explore Module
Track Progress
0 / 6Projects Verified
Learning Outcomes
- Compare cosine, dot product, and Euclidean distance vector metrics
- Implement semantic and parent-child chunking parsers
- Configure HNSW graph indexing configurations
Interview Defense
- Explain recall vs latency tradeoffs in approximate nearest neighbor search
- Defend dense embeddings search vs sparse BM25 indexing in enterprise catalogs