Embeddings

What are Embeddings?

An embedding model reads a piece of content and outputs a vector: a long list of numbers that places the content in a mathematical space. Items with similar meaning land close together. "How do I reset my password?" and "I forgot my login" end up near each other even though they share almost no words, while "password policy for admins" lands nearby but distinct.

Using embeddings takes two steps. First, convert your content, such as help articles, product descriptions or support tickets, into vectors and store them, often in a vector database. Second, when a query arrives, convert it with the same model and find the stored vectors closest to it.

In a business, embeddings sit behind search that understands intent rather than keywords, "similar products" and "related articles" features, duplicate detection in tickets or CRM records, grouping customer feedback into themes, and the retrieval step that feeds company knowledge to AI assistants.

What are embeddings used for?

Because embeddings measure similarity, they appear in many features that need to find related content:

• Semantic search: finding documents by meaning, so users do not need the exact words.
• Retrieval for AI: selecting the passages a language model should read, the core of retrieval-augmented generation.
• Recommendations: suggesting products, articles or content similar to what someone viewed.
• Deduplication and matching: spotting near-identical records, tickets or listings.
• Clustering and classification: grouping feedback or requests into themes, often as input to text analytics.

Each use relies on the same idea: turning content into numbers so that closeness in meaning can be measured and ranked. The same stored vectors can often serve several of these features at once.

How do embeddings work with search?

Traditional keyword search matches the words in a query with the words in documents. It is fast and precise for exact terms such as product codes or names, but misses synonyms and paraphrases. Embedding search, often called vector or semantic search, matches meaning, so it handles paraphrases well but can miss exact identifiers or return results that are related but not quite right.

Most production systems use both. A hybrid search runs keyword and vector queries together and merges the results, and a re-ranking step then orders the top candidates more carefully. Filters on metadata, such as language, product line, date or user permissions, narrow the results before or after the similarity search.

The outcome is search that understands how people actually phrase questions while still respecting exact terms and access rules.

How do you choose an embedding model?

Test candidates on your own content and real queries. Build a small set of questions with the documents that should come back, then check which model ranks the right ones highest. Public benchmarks help narrow the list, but your terminology, languages and document types decide the result.

Other factors matter in practice: support for the languages your users write in, the maximum length of text each model accepts, the size of each vector, which drives storage and search speed, the cost per document, and whether the model can run where your data must stay.

One rule is easy to miss: queries and documents must be embedded with the same model. Switching models means re-embedding the whole collection, so keep the source text and a repeatable pipeline from the start.

What are the limits of embeddings?

Embeddings capture similarity, not truth or relevance to a specific question. Two passages can be close in meaning while only one actually answers what was asked, which is why re-ranking and good prompts still matter in retrieval.

They also flatten detail. Long documents squeezed into a single vector lose specifics, so content is usually split into smaller chunks before embedding. Numbers, negations and exact codes are handled poorly: a search for one product version may return its neighbor.

Finally, embeddings inherit the data and biases of the model that produced them, and they go stale when source content changes. A pipeline that re-embeds updated documents and removes deleted ones is part of keeping any embedding-based feature accurate over time.

How does Vizio AI build with embeddings?

Our Venture Studio uses embeddings to build AI-enabled product features: semantic search, related content and product recommendations, duplicate detection, and the retrieval layer behind in-product assistants and internal tools. We choose and test embedding models on your own content, design how documents are split and stored, combine vector and keyword search where exact terms matter, and set up the pipeline that keeps the index current as content changes. This sits inside the SaaS platform architecture so it can grow with the product.

The usual starting point is a set of real queries and the results a user would expect, which shows whether embeddings beat the current search.