AI Skills & How They Work
Vector Embeddings Demystified
Embeddings turn text, images, or other data into lists of numbers so that similar items land close together in a high-dimensional space. That simple idea powers semantic search, recommendations, and RAG. The practical work is choosing models, chunking well, and measuring retrieval quality, not marveling at the math.
Points clés
- An embedding is a coordinate: each item becomes a vector, and distance between vectors approximates semantic similarity.
- Models are trained for similarity: contrastive learning pulls related items together and pushes unrelated ones apart.
- Chunking decides retrieval: too large and you miss specifics; too small and you lose context; tune on your data.
- Cosine similarity is the default metric: most embedding models are normalized, so cosine and dot product agree.
- Dimensions are a trade-off: more dimensions capture nuance but cost more storage and search time.
- Embeddings are not facts: they encode similarity, not truth; a wrong document can still be the closest match.
- Evaluation is retrieval recall: measure whether the right documents appear in the top-k results on a labeled set.
- Re-embedding is a migration: changing models means recomputing every vector, so plan for it before you scale.
Questions fréquentes
Are embeddings the same as keywords?
No. Keywords match exact terms; embeddings match meaning, so synonyms and paraphrases retrieve correctly.
How many dimensions do I need?
Use the model's default; reducing dimensions saves cost but usually costs recall.
Can I embed structured data?
Yes, by serializing it to text or using specialized models; quality depends on representation.
Why does retrieval return irrelevant results?
Usually chunking or query mismatch; fix the data before blaming the model.
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