Embedding Models

An embedding model is a separate, specialized model from a general-purpose LLM — trained specifically to produce vectors where distance reflects semantic similarity, not to generate fluent text.

Embedding Models vs LLMs — Different Jobs

Some providers offer embedding models as a completely separate product/API from their chat/generation models — worth checking whether your provider's embedding model is a distinct, purpose-built model rather than assuming it's "the same model" used for chat.

Embedding Models Differ From Each Other Too

• Dimensionality — different models output vectors of different sizes (see Embedding Dimensions)
• Training domain — some are general-purpose; others are tuned for specific domains (code, multilingual text, specific industries)
• Max input length — embedding models have their own input length limits, separate from any LLM's context window

Important: Vectors From Different Models Aren't Interchangeable

# WRONG — comparing vectors from two different embedding models
vector_a = model_A.embed("some text")
vector_b = model_B.embed("other text")
similarity = cosine_similarity(vector_a, vector_b)  # meaningless —
                                                       # different
                                                       # models produce
                                                       # incompatible
                                                       # vector spaces

All vectors compared to each other in a search/retrieval system must come from the same embedding model — mixing models (or switching models without re-embedding existing content) silently breaks similarity comparisons.

Practical Use Case

Choosing an embedding model is a real decision — general-purpose models work well for most applications, but a domain-specific model (e.g. one trained on code, or on a specific language) can meaningfully outperform a general model for that specific use case.

Common Mistakes

• Switching embedding models without re-embedding all previously stored content — old and new vectors become incompatible, silently corrupting search quality
• Assuming a general-purpose embedding model is automatically the best choice for a specialized domain (like source code) without testing

Interview Relevance

"If you switch your embedding model, what has to happen to your existing vector database?" — every previously stored vector must be regenerated with the new model; old and new vectors aren't comparable, a commonly overlooked migration cost.

Practice Question

Explain why a support-search system that switched embedding models but forgot to re-embed its existing 50,000 stored documents would start returning poor search results.

Related Notes

• Text Embeddings
• Embedding Dimensions
• Vector Database