When an AI assistant finds the right paragraph in thousands of documents, embeddings and vector databases are usually doing the work behind the scenes.
What is an embedding?
An embedding turns a piece of text into a list of numbers that captures its meaning. Texts with similar meanings end up with similar numbers, even if they use different words.
What is a vector database?
A vector database stores those numbers and can quickly find the items closest in meaning to a question. It is a searchable index built for meaning rather than exact keywords.
How it fits into RAG
- Your documents are split into small sections and converted to embeddings
- The sections are stored in a vector database
- When a user asks a question, it is converted the same way
- The closest sections are retrieved and given to the LLM to write the answer
Why it beats keyword search
A customer asking about “getting my money back” can match a policy titled “Refunds and returns”, even though the words differ.
Things to get right
- Split documents sensibly so each section makes sense alone
- Keep content up to date and remove outdated files
- Control who can access which documents
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