RAG-Standard (standard Retrieval-Augmented Generation) combines a language model with a retrieval layer that fetches relevant documents from a knowledge store at query time. Retrieved chunks are embedded into the model’s prompt so the LLM can ground its answers in up-to-date, domain-specific data instead of relying only on pretraining. This pattern is typically implemented as a single-turn or lightly multi-turn pipeline: embed query, retrieve top-k documents, construct a prompt, and generate an answer. It is the default architecture for enterprise Q&A, knowledge assistants, and search-style applications.

Canonical solution label for solution rows that describe the business outcome of predictive analytics at a family level without specifying the underlying modeling technique.

The time-series pattern focuses on modeling data that is indexed by time to capture temporal dependencies, trends, and seasonality. It uses statistical, machine learning, and increasingly foundation-model-based approaches to forecast future values, detect anomalies, and understand temporal patterns. Models typically leverage lagged values, rolling windows, temporal embeddings, and exogenous variables to learn how past and contextual signals influence future behavior. This pattern underpins operational forecasting, monitoring, and control in many data-driven systems.