Embeddings

01why it matters for a business

Keyword search fails when people describe the same thing differently: a customer writes that a unit is leaking while the manual talks about condensate overflow. Embeddings let a system match on meaning, which is why they sit underneath most RAG systems, semantic search, duplicate detection, and content recommendations. They are cheap to compute compared with generating text, so they scale to very large document collections.

For decision makers, the practical points are consistency and fit. Embeddings from one model are not comparable with embeddings from another, so changing embedding models means reprocessing your content. General-purpose embedding models can struggle with specialized jargon, part numbers, and codes, which is why strong systems combine embeddings with keyword search rather than relying on either alone.

02what it looks like in practice

A B2B distributor's site search returns nothing when buyers type plain-language descriptions instead of part numbers. The team creates embeddings for every product description and spec sheet. A search for a quiet exhaust fan for a small bathroom now returns the right products, ranked by similarity in meaning. Exact part-number searches still go through keyword matching, and results from both are merged, so neither kind of query breaks.

03common mistakes

• Mixing embeddings from different models in one index.
• Relying on embeddings alone for identifiers such as SKUs, codes, and names, where exact matching works better.
• Embedding huge documents as single items. Split content into meaningful sections so matches point to the right passage.
• Never re-evaluating. Test retrieval quality on real queries whenever content or models change.

04related terms

• Vector databaseA vector database is a database designed to store embeddings and quickly find the ones most similar to a query, a task called similarity or nearest-neighbor search.
• Retrieval-augmented generation (RAG)Retrieval-augmented generation (RAG) is a technique in which an AI system first searches your own documents or data for passages relevant to a question, then gives those passages to a language model to write its answer.
• AI knowledge baseAn AI knowledge base is a company's documents, policies, procedures, and records organized so an AI assistant can search them and answer questions with citations.
• Large language model (LLM)A large language model (LLM) is an AI model trained on very large amounts of text to predict the next piece of text, which lets it write, summarize, translate, classify, extract information, and reason through problems in everyday language.

05where insomnia club fits