RAG explained

What is retrieval-augmented generation (RAG)?

RAG is an AI architecture that retrieves relevant information at request time and gives that evidence to a language model so it can produce a more grounded, current, and attributable response.

Build a RAG system

Source-connected

Permission-aware

Measured before launch

How RAG works

A user submits a question. The application converts that question into one or more searches against approved sources. It selects relevant passages or records, assembles them as context, and instructs a language model to answer using that evidence. The response can include citations that let the user inspect the original sources.

Before any question is asked, documents usually must be collected, parsed, divided into useful units, enriched with metadata, represented for search, and stored in an index. Structured systems may be queried through APIs or purpose-built tools rather than copied into a vector index.

Why organizations use RAG

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Use private or proprietary knowledge without retraining a model

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Reflect source updates through an ingestion process

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Provide citations and links to supporting evidence

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Restrict retrieval based on user and document permissions

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Improve responses for specialized language and workflows

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Measure and tune retrieval separately from generation

What RAG does not solve automatically

RAG does not guarantee truth. The search may miss the right source, retrieve an outdated version, or select insufficient context. A model may misinterpret good context or make unsupported claims. Source quality, permissions, prompt injection, latency, cost, and user experience still require deliberate engineering.

RAG examples

Knowledge assistant

Answers employee questions from approved policies and procedures with citations.

Service copilot

Retrieves product documentation and similar cases while a support agent handles an issue.

Document analyst

Finds and compares relevant clauses, requirements, or technical passages across a controlled collection.

From demo to production RAG

A production system needs repeatable ingestion, document lifecycle handling, identity, source permissions, evaluation, monitoring, feedback, and operational ownership. The best starting point is a bounded workflow with representative questions and authoritative sources.

Continue learning about RAG

Understanding the definition is only the beginning. Architecture choices, evaluation, security, and the difference between a compelling example and a supportable production system determine whether RAG creates value.

Questions

Frequently asked questions

RAG stands for retrieval-augmented generation. It retrieves relevant information and supplies it to a generative model when producing a response.

An LLM is a model. RAG is an application architecture that combines a model with external retrieval so responses can use selected information at request time.

Normally no. RAG retrieves your data as context at request time. Fine-tuning changes model behavior or weights and is a different technique, although the two can be combined.

A vector database stores numeric representations of content and supports similarity search. It is one possible retrieval component, often combined with keyword search and metadata filtering.

A practical next step

Bring us the workflow that is stuck—not a finished AI specification.

We will help clarify the opportunity, data, risks, and smallest useful way to prove value.

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