RAG Implementation

Retrieval-Augmented Generation (RAG) is a method that augments generative models with external retrieval of documents to ground responses in factual knowledge. It combines a retriever and a generator to improve accuracy and context-awareness. This method guides architecture, data pipelines, and evaluation for knowledge-intensive applications.

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Content type

Method

Executable approach: can be applied and produces an outcome.

Classification level

Structure

What organizes, connects, or makes decisions possible.

Available perspectives

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360°

In brief

RAG implementation – in brief

A RAG implementation combines document retrieval with a generative model so answers can draw on selected external knowledge sources.

Where does it come from?

Retrieval-Augmented Generation was described as a research approach by Patrick Lewis and co-authors in 2020. Practice applied it to search indexes, embeddings, and application pipelines; Microsoft documents current architecture variants.

The mental model

Chunk and index trusted documents, create useful representations, and retrieve the most relevant passages for each query. Pass them to the model with clear instructions and expose sources or locations. Measure retrieval quality, answer faithfulness, latency, and cost separately; treat absent evidence as a possible stop condition.

Key building blocks

Knowledge index

Prepared document chunks and metadata enable targeted search for a query.

Retrieval

A retriever selects relevant evidence before the model generates an answer.

Grounding

The answer should rely on retrieved context and avoid unsupported claims.

When is it useful?

RAG can expose domain-specific and current knowledge without fully retraining a model. Quality depends on sources, chunking, search, and evaluation; retrieval alone guarantees neither truth nor privacy.

Microsoft Learn: Retrieval-augmented generation overview

Why is this building block relevant?

A practical guide for implementing Retrieval-Augmented Generation (RAG).
Describes architecture patterns, data flows, and evaluation criteria for knowledge-grounded generative AI systems.