Retrieval and model behavior
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.
Embeddings
Embeddings are numerical representations of meaning that let systems compare how similar two pieces of text (or images) are, even when wording differs.
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-Augmented Generation (RAG)
Retrieval-augmented generation (RAG) retrieves relevant documents or records first, then asks the model to answer using that material.
Semantic Search
Semantic search finds content by meaning and intent, not just exact keyword matches, powered by embeddings and vector comparison.
Vector Database
A vector database stores embeddings and retrieves the nearest matches quickly: the infrastructure behind semantic search and RAG at scale.