AI UX RAG and Retrieval Glossary
How products find relevant knowledge, fit it into a model, and return answers grounded in your data, not generic training text.
6 terms
- Retrieval and model behavior
Context Window
The context window is the maximum amount of text (in tokens) a model can consider in one request: your prompt, system instructions, retrieved docs, and chat history combined.
- Retrieval and model behavior
Embeddings
Embeddings are numerical representations of meaning that let systems compare how similar two pieces of text (or images) are, even when wording differs.
- Retrieval and model behavior
Grounding (UX)
Grounding in UX is how an interface ties AI answers to verifiable sources—documents, URLs, files, or tool results—so users can check claims.
- Retrieval and model behavior
Retrieval-Augmented Generation (RAG)
Retrieval-augmented generation (RAG) retrieves relevant documents or records first, then asks the model to answer using that material.
- Retrieval and model behavior
Semantic Search
Semantic search finds content by meaning and intent, not just exact keyword matches, powered by embeddings and vector comparison.
- Retrieval and model behavior
Vector Database
A vector database stores embeddings and retrieves the nearest matches quickly: the infrastructure behind semantic search and RAG at scale.