AI
Vector Databases
A practical guide to understanding what vector databases are and why they matter for AI search and retrieval.
What vector databases do, how they enable semantic search and RAG, and what product and design teams need to know when working with AI systems that use them.
What it is
A glossaryVector DatabaseA vector database stores and retrieves data based on vector representations, enabling similarity search and retrieval.Open glossary term is a type of database designed to store and glossarySearchSearch is the functionality that allows users to find content or information by entering queries. It relies on indexing, metadata, and relevance algorithms to return useful results.Open glossary term through guideEmbeddingsWhat embeddings do, how they allow AI to understand meaning rather than just match words, and what product teams need to know when working with semantic search or RAG.Open guide — the numerical representations of text (or other content) that capture meaning.
Traditional databases store glossaryStructured DataStructured data is a standardised format used to organise and label content so it can be easily understood by search engines and AI systems.Open glossary term and glossarySearchSearch is the functionality that allows users to find content or information by entering queries. It relies on indexing, metadata, and relevance algorithms to return useful results.Open glossary term by exact match or simple criteria. glossaryVector DatabaseA vector database stores and retrieves data based on vector representations, enabling similarity search and retrieval.Open glossary term store vectors and search by similarity — finding the items closest in meaning to a given query.
When a user asks a question in an AI-powered glossarySearchSearch is the functionality that allows users to find content or information by entering queries. It relies on indexing, metadata, and relevance algorithms to return useful results.Open glossary term or chat glossaryFeatureA feature is a specific piece of functionality within a product that delivers value to users. It represents something users can do or experience as part of the overall product.Open glossary term, their query is converted into a vector and compared against vectors stored in the database. The items closest to the query vector are retrieved and passed to the language glossaryModelA model is a system or representation used to process data and generate outputs, often trained to perform specific tasks.Open glossary term as relevant context.
glossaryVector DatabaseA vector database stores and retrieves data based on vector representations, enabling similarity search and retrieval.Open glossary term are the glossaryInfrastructureInfrastructure refers to the underlying systems and resources that support applications and services.Open glossary term that makes guideRetrieval-Augmented Generation (RAG)What retrieval-augmented generation is, how it improves AI accuracy, and what designers and product teams need to know when working with it.Open guide work at scale. Without them, a RAG system would need to compare every piece of content against every query — which is computationally impractical for large knowledge bases.
When to use it
Understand when glossaryVector DatabaseA vector database stores and retrieves data based on vector representations, enabling similarity search and retrieval.Open glossary term are relevant to your product. They are most relevant when:
They are less relevant when:
Key takeaway
If your AI product needs to find relevant information from a large knowledge base, a vector database is almost certainly part of the solution.
How it works
Understand the basic mechanism. Content is first processed through an embedding glossaryModelA model is a system or representation used to process data and generate outputs, often trained to perform specific tasks.Open glossary term, which converts each piece of text into a vector. These vectors are stored in the glossaryVector DatabaseA vector database stores and retrieves data based on vector representations, enabling similarity search and retrieval.Open glossary term.
When a glossarySearchSearch is the functionality that allows users to find content or information by entering queries. It relies on indexing, metadata, and relevance algorithms to return useful results.Open glossary term query arrives, it is also converted into a vector using the same embedding glossaryModelA model is a system or representation used to process data and generate outputs, often trained to perform specific tasks.Open glossary term. The glossaryVector DatabaseA vector database stores and retrieves data based on vector representations, enabling similarity search and retrieval.Open glossary term then performs a similarity search — finding the stored vectors that are mathematically closest to the query vector.
The retrieved content is then passed to the language glossaryModelA model is a system or representation used to process data and generate outputs, often trained to perform specific tasks.Open glossary term, which uses it to generate a relevant, grounded glossaryResponseA response is the data or result returned by a server after receiving a request.Open glossary term.
What this means for designers and product teams. glossaryVector DatabaseA vector database stores and retrieves data based on vector representations, enabling similarity search and retrieval.Open glossary term are glossaryInfrastructureInfrastructure refers to the underlying systems and resources that support applications and services.Open glossary term — they are not something designers directly interact with. But the design decisions around them matter: what content is stored, how it is structured, and how retrieval quality is evaluated all have a direct impact on the serviceUser ExperienceImprove journeys, reduce friction, and make digital products clearer, simpler, and easier to move through.Open service.
Poor retrieval — surfacing the wrong content for a query — is one of the most common failure modes in RAG-powered glossaryFeatureA feature is a specific piece of functionality within a product that delivers value to users. It represents something users can do or experience as part of the overall product.Open glossary term, and it is often a content or structure problem rather than a database one.
What to look for
Focus on:
Where it goes wrong
Most issues come from: Retrieval failures are usually content problems masquerading as technology problems.
What you get from it
Understanding glossaryVector DatabaseA vector database stores and retrieves data based on vector representations, enabling similarity search and retrieval.Open glossary term gives you:
Key takeaway
Vector databases make semantic search possible at scale. Understanding their role helps you design better content strategies and evaluate AI search quality more effectively.
FAQ
Common questions
A few practical answers to the questions that usually come up around this method.
What is a vector database?
A glossaryVector DatabaseA vector database stores and retrieves data based on vector representations, enabling similarity search and retrieval.Open glossary term is a type of database designed to store and glossarySearchSearch is the functionality that allows users to find content or information by entering queries. It relies on indexing, metadata, and relevance algorithms to return useful results.Open glossary term through guideEmbeddingsWhat embeddings do, how they allow AI to understand meaning rather than just match words, and what product teams need to know when working with semantic search or RAG.Open guide — numerical representations of content that capture meaning rather than just text. It enables similarity search, finding the content most relevant to a query based on meaning rather than keyword matching.
How is a vector database different from a regular database?
A regular database stores glossaryStructured DataStructured data is a standardised format used to organise and label content so it can be easily understood by search engines and AI systems.Open glossary term and glossarySearchSearch is the functionality that allows users to find content or information by entering queries. It relies on indexing, metadata, and relevance algorithms to return useful results.Open glossary term by exact match or defined criteria. A glossaryVector DatabaseA vector database stores and retrieves data based on vector representations, enabling similarity search and retrieval.Open glossary term stores vectors and searches by mathematical similarity — finding items that are closest in meaning to a given query, even if they share no common words.
Do I need to understand vector databases to design AI features?
Not technically, but you benefit from understanding their role. If your product uses AI-powered glossarySearchSearch is the functionality that allows users to find content or information by entering queries. It relies on indexing, metadata, and relevance algorithms to return useful results.Open glossary term or RAG, the quality of that glossaryFeatureA feature is a specific piece of functionality within a product that delivers value to users. It represents something users can do or experience as part of the overall product.Open glossary term depends on what is stored in the glossaryVector DatabaseA vector database stores and retrieves data based on vector representations, enabling similarity search and retrieval.Open glossary term and how well the retrieval works. That is relevant to content strategy, information architecture, and quality evaluation — all design concerns.
What are some examples of vector database tools?
Pinecone, Weaviate, Qdrant, Chroma, and pgvector are among the most widely used. The choice between them is typically an engineering decision based on glossaryPerformancePerformance refers to how quickly and efficiently a system responds to user actions and processes tasks.Open glossary term, cost, and glossaryInfrastructureInfrastructure refers to the underlying systems and resources that support applications and services.Open glossary term requirements.
Can a vector database go out of date?
Yes. Vectors are generated from a snapshot of the content at a point in time. If the source content changes and the vectors are not updated, the database will return outdated results. Keeping vectors in sync with content changes is an important operational responsibility.
Quick take
Vector databases are what make it possible for AI to search and retrieve information based on meaning rather than keywords — and they are a core piece of infrastructure in many AI products.
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