Vector Databases

Generative AI applications, especially those involving large language models (LLMs), increasingly rely on vector databases to enhance their capabilities. In use cases like retrieval-augmented generation (RAG), semantic search, and recommendation systems, vector databases provide a way to inject relevant knowledge or find similar items based on meaning rather than an exact keyword.

Vector databases are becoming foundational in these GenAI scenarios because they enable low-latency similarity queries at scale, something not feasible with classic relational databases. It won’t be wrong to say that shortly, most enterprises will have adopted vector databases to build their foundation models with relevant business data. As a result, questions about vector databases are now common in interviews for AI-specific roles and increasingly in general technical screenings, reflecting their growing importance across the industry. In summary, as AI applications deal with unstructured data (text, images, audio) and require semantic understanding, vector databases provide the backbone for storing and retrieving that data in a form that AI models can effectively use. Next, we’ll define a vector database and how it differs from traditional databases.