Embeddings are numerical representations of text or other content like images or audio that capture meaning in a form a machine can process and compare. When text is converted into an embedding, it becomes a series of numbers that position that text in a high-dimensional space. Content that is semantically similar ends up positioned close together in that space, even if the words used are completely different.
This is what allows an AI system to understand that “how do I reset my password” and “I can’t log in” are related questions without the two phrases sharing any words. The meaning is encoded in the numbers, not the text itself.
Embeddings are foundational to how many AI search and retrieval systems work, including RAG architectures. Rather than searching for exact keyword matches, these systems compare embeddings to find content that is conceptually relevant to a query.
Why This Matters for Technical Writers
Embeddings are part of what determines whether an AI search or retrieval system surfaces the right content in response to a user query. If you are documenting an AI search feature, a knowledge assistant, or a RAG-based system, understanding embeddings helps you explain why results are relevance-based rather than keyword-based — and why the system may return unexpected results when content is ambiguous, poorly structured, or inconsistently written. Documentation quality directly affects embedding quality: content that is vague or inconsistent produces embeddings that are harder to match accurately.
Common Confusion
Embeddings are sometimes confused with the model itself, but they are an output of the model rather than the model. A language model generates embeddings; a separate system, typically a vector database, stores and searches them. Embeddings are also not the same as keywords or tags. They encode meaning rather than surface-level terms, which is both their strength and the reason their behavior can be less predictable than a keyword search.
Related Terms
RAG, AI search and retrieval, large language model (LLM), vector database, semantic search, token, foundation model