AI Engineering
Learn LLMs, RAG, agents, secure tools, and production AI through practical insurance examples.
Embeddings, vectors, dimensions, and semantic similarity
An embedding is a list of numbers representing useful features of content, often its meaning. That list is a vector; its number of entries is its dimension. Semantic similarity means closeness in meaning, such as “car accident” and “vehicle collision,” even…
Similarity and cosine similarity
Similarity measures how close two vectors are according to a selected metric. Cosine similarity compares their direction, effectively asking whether they point in a similar direction regardless of magnitude. Dot product and Euclidean distance are other common…
Embedding models, generation, storage, and limitations
An embedding model creates vectors; an embedding pipeline manages generation, versions, and storage. An embedding is a derived index representation, not a replacement for the original text. Different models, dimensions, or preprocessing choices can produce…
Explain an embedding.
It is a numerical vector that represents useful features of content, often meaning. Similar wording or meaning tends to produce nearby vectors, enabling retrieval of “vehicle collision” for a query about “car accident.”
What does cosine similarity tell you?
It compares vector direction and helps rank related content. A high score is not a probability that the passage is true or sufficient. I use a compatible metric and calibrate thresholds on domain-specific queries.
How do you upgrade embedding models?
I version models and index data, re-embed the intended corpus into a separate compatible space, evaluate filtered retrieval, and switch query/index routing together. I preserve source data and support rollback instead of mixing arbitrary old and new vectors.
Policy-code search fails despite good semantic search. What do you do?
I add exact identifier fields and lexical/hybrid retrieval. Embeddings may not preserve rare codes or precise numbers well. I test actual identifiers and keep them intact during query rewriting and text preprocessing.
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