Grounding and RAG

Definition

Grounding and RAG

Grounding is the practice of constraining model output with task-relevant evidence from trusted sources, live system state or authoritative tools. Retrieval-augmented generation is a common grounding pattern in which the system retrieves candidate passages or records, injects selected context into the model call and evaluates whether answers are supported by that evidence.

Why It Matters

Grounding connects model output to trusted, task-relevant sources or system state. It reduces unsupported generation and enables answers that reflect current or private information.

Retrieval-augmented generation is a pattern in which relevant information is retrieved and supplied to the model during inference. A RAG pipeline typically covers ingestion, parsing, chunking, metadata, indexing, retrieval, reranking, context assembly, generation and citation.

Failure mode

The recurring risk is treating this concept as a label rather than an engineering decision with trade-offs, evidence needs and operational consequences.

Evaluation check

Ask what observable evidence would show that this concept has been applied well in a real system, repository or learning exercise.

Related Concepts

• Embeddings, vector search, graphs and semantic layers
• Evals and error analysis

Further study: Lewis et al., Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, and OpenAI, Retrieval.