Embeddings and Vectors¶
An embedding turns text into a list of numbers that carries its meaning, so "how do I cancel" lands near "unsubscribe from my plan" even though they share no words. This subtopic is the concept: what vectors are, what they're good for, and how they fail.
flowchart LR
J["Junior: text to vector, similarity"] --> M["Middle: what you can build"]
M --> S["Senior: similarity vs relevance"]
S --> P["Professional: own the pipeline"]
Levels¶
| Level | Guide | You are done when |
|---|---|---|
| Junior | Text to vector | You can explain what a vector is, how text becomes one, and why cosine similarity measures relatedness. |
| Middle | What you can build | You can name four things vectors enable, and when plain keyword search beats them. |
| Senior | Similarity vs relevance | You can diagnose a vector search that returns similar-but-wrong results, with a metric. |
| Professional | Own the pipeline | You can plan re-embedding migrations and decide when to retire a vector pipeline. |
Practice rule¶
An embedding model is a separate model from your chat model — different vendor, different cost, different quality bar. Never assume your chat model's quality tells you anything about its embedding sibling.
Related¶
- RAG and Vector Decisions — the production retrieve→augment→generate pipeline and when RAG earns its cost.
- Search and Retrieval — the keyword techniques to try first.