Embeddings as Vectors: Length, Normalisation & the L1 Norm
All lessons
- Embeddings as Vectors: Length, Normalisation & the L1 NormFree
- Dot Product vs Cosine Similarity with SUMPRODUCT Pro
- Top-k Retrieval: RANK.EQ, LARGE and INDEX/MATCH Pro
- Euclidean Distance vs Cosine: When Do the Rankings Disagree? Pro
- Chunking: Tokens per Chunk, Number of Chunks & Embedding Cost Pro
- Approximate Search: Recall@k vs Exact Search and the Speed Trade-off Pro
- Hybrid Search: Blending Keyword and Vector Scores Pro
- RAG Retrieval Evaluation: Hit Rate@k, MRR & a Chart Pro
You are not signed in — the lab works, but your progress is not saved. Start free
Loading the spreadsheet…