Retrieval-Augmented Generation

Definition

Retrieval-augmented generation retrieves relevant reference material before or during a model request and provides that material as context for generation.

Why it matters to the business

It can make an AI-assisted workflow use current internal documents or records without treating the model's pretraining as the business source of truth.

How it works

A retrieval step selects relevant material from an approved source, then the application includes that context in the model request and applies its own output controls.

Where it appears in real systems

It can support document Q&A, support-assistant drafts, internal knowledge workflows and structured extraction from approved corpora.

Common failure modes

• Retrieval returns irrelevant material.
• The source corpus is stale.
• Permissions are ignored.
• The model overstates what the retrieved evidence supports.

Implementation considerations

• Define source ownership.
• Control document access.
• Evaluate retrieval quality separately from generation quality.
• Provide fallback when relevant evidence is absent.

When it is appropriate

Use retrieval when the workflow needs changing or private reference material at run time.

When it is not appropriate

Do not add retrieval where the task is deterministic, the reference set is tiny enough for simpler handling, or the source material is not trustworthy.

Relationship to BYFORCE

RAG may be used inside an AI Workflow Production Sprint when the bounded use case genuinely needs external reference context.