How much does RAG development cost?

A proof-of-concept starts at $1,500 to $4,000. A full production RAG system ranges from $8,000 to $30,000 depending on complexity and data volume.

How long does it take to build a RAG system?

A proof-of-concept typically takes 2 to 4 weeks. A full production system with guardrails, evaluation, and deployment typically takes 4 to 10 weeks depending on data complexity and integration scope.

The architecture behind grounded AI

What is RAG (retrieval-augmented generation)? How it works and why it matters

Q: What does RAG stand for?

RAG stands for retrieval-augmented generation, an AI architecture that retrieves relevant information before an LLM generates its response, rather than relying only on the model's training data.

Q: Is RAG the same as fine-tuning?

No. Fine-tuning changes a model's weights through additional training. RAG retrieves external information and adds it to the model's context at query time without changing the model. They solve different problems and are often used together.

Q: Does RAG eliminate AI hallucination?

RAG significantly reduces hallucination, typically 60 to 80% versus a bare LLM, by grounding responses in retrieved content, but does not eliminate it entirely. Guardrails and confidence thresholds are layered on top in every production system.

Retrieval-augmented generation (RAG) is an AI architecture that retrieves relevant information from an external knowledge source before a large language model generates an answer. This allows AI applications to work with current, private, or domain-specific information instead of relying only on the model's training data.

Approach Retrieval-grounded answers

Traceability Sources can be cited

Knowledge External data at query time

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Learn how retrieval connects your data with an LLM at query time

grounding preview, illustrative live

Low Hallucination risk

4 Chunks retrieved

Live docs Answer basis

Generated answer

Switch modes to see what each approach provides and what it cannot.

Grounded, not guessed RAG retrieves relevant information from external knowledge sources before the model generates an answer.

Source-traceable Retrieved documents can be included with the response so users can review where the information came from.

Works with changing data Knowledge sources can be updated without retraining the underlying language model.

Useful for private data RAG can connect an AI application to internal documents, knowledge bases, product information, and other external sources.

Plain answer

What is RAG (retrieval-augmented generation)?

Definition

Retrieval-augmented generation (RAG) is an AI architecture that improves large language model responses by retrieving relevant information from an external knowledge base at query time and providing that information to the model as context before it generates an answer. Instead of relying only on information learned during training, the application can use current, private, or domain-specific source content when responding to a user.

Step by step

How RAG works

A typical RAG system retrieves relevant information first and then provides that information to the language model as context for generating the answer.

STEP 1

Query submitted

A user asks a question in natural language.

STEP 2

Query embedded

The query is converted into a numerical representation that captures its semantic meaning.

STEP 3

Relevant content retrieved

Search retrieves relevant chunks from a knowledge base or vector database.

STEP 4

Context inserted

Retrieved content is added to the query and supplied to the LLM as context.

STEP 5

Grounded answer

The LLM generates a response using the retrieved information, with sources available when the system supports citations.

The decision

RAG vs plain LLM vs fine-tuning

RAG, plain language models, and fine-tuning solve different problems. They can also be combined when an application needs both task-specific behavior and access to current or proprietary information.

Factor	Plain LLM	RAG	Fine-tuning
Knowledge source	Training data	Training data plus retrieved external content	Training data plus task-specific training examples
Update process	Model updates require new training or a newer model	Update the external knowledge source without retraining the underlying model	Requires additional training when model behavior needs to be changed
Best for	General language and knowledge tasks	Current, domain-specific, or private information	Task-specific behavior, style, or output patterns
Traceability	No retrieval source by default	Retrieved sources can be cited	No retrieval source by default

Core concepts

What makes a RAG system work?

A RAG application is more than connecting an LLM to a vector database. The quality of retrieval, source preparation, context selection, and evaluation all influence the final response.

Document ingestion

Source material such as PDFs, web pages, wikis, databases, and other documents is collected and prepared for retrieval.

Chunking

Large documents are divided into smaller pieces so the retrieval system can find information relevant to a particular question.

Embeddings

Text can be converted into numerical representations that allow semantically similar queries and documents to be found.

Retrieval

The system searches the knowledge source and selects the information most relevant to the user's question.

Context and generation

Relevant retrieved content is provided to the language model so it can generate an answer using that context.

Evaluation

Retrieval and answer quality should be evaluated against representative questions before the system is relied upon in production.

Why it matters

Why is RAG useful?

A language model's training data does not automatically provide access to an organization's private documents, changing product information, internal policies, or other external knowledge. RAG provides a way for an AI application to retrieve relevant information at query time and use it as context when generating a response.

COMMON APPLICATIONS

Where RAG can be used

Customer support assistants grounded in product documentation and policies
Internal knowledge assistants for employee questions over wikis and company documents
Healthcare AI assistants grounded in clinical guidelines — see the healthcare page
Legal and compliance research tools that retrieve relevant clauses or documents
Ecommerce product assistants answering questions from product information
Enterprise search systems that retrieve relevant information from large document collections

Practical guide

When should you use RAG?

RAG is particularly useful when an AI application needs access to information that is private, frequently changing, or specific to a particular domain.

USE CASE 01

Private company knowledge

Use RAG when an assistant needs to answer questions using internal documents, policies, manuals, or company knowledge.

USE CASE 02

Frequently changing information

RAG can retrieve updated information without requiring the underlying language model to be retrained whenever documents change.

USE CASE 03

Source-based answers

RAG is useful when users need answers that can be connected back to the documents or sources used by the application.

Build with RAG

Need a RAG application for your business?

If you are planning a production RAG application, you can learn more about the development process, architecture, and engagement options on the dedicated RAG development page.

Hire a RAG developer

RAG pipeline

From documents to grounded answers

STEP 01 source

Prepare knowledge

Documents and other knowledge sources are collected, cleaned, parsed, and prepared for retrieval.

STEP 02 index

Create searchable representations

Content is divided into useful chunks and indexed so that relevant information can be retrieved efficiently.

STEP 03 retrieve

Find relevant context

A user's query is used to retrieve the most relevant pieces of information from the connected knowledge source.

STEP 04 generate

Generate the answer

The retrieved information is provided to the language model as context so it can generate a response.

STEP 05 evaluate

Evaluate the result

The retrieval and generated responses can be evaluated against representative questions to identify gaps and improve quality.

FAQ

Frequently asked questions about RAG

What does RAG stand for?

RAG stands for retrieval-augmented generation. It is an AI architecture that retrieves relevant external information and provides it to a language model as context before the model generates a response.

Is RAG the same as fine-tuning?

No. Fine-tuning changes a model's behavior through additional training, while RAG retrieves external information and adds it to the model's context at query time. They solve different problems and can also be used together.

Does RAG eliminate AI hallucination?

No. RAG can reduce the risk of unsupported answers by providing relevant source information to the model, but it does not guarantee that every generated response will be correct. Retrieval quality, prompt design, evaluation, and appropriate guardrails are still important.

What type of data can RAG use?

RAG systems can retrieve information from sources such as documents, PDFs, knowledge bases, websites, product catalogs, internal wikis, databases, and other structured or unstructured data sources, depending on the system architecture.

Does RAG require retraining the language model?

No. A key advantage of RAG is that the external knowledge source can be updated without retraining the underlying language model. The application retrieves the latest available information when processing a query.

When should I use RAG instead of fine-tuning?

RAG is generally useful when the application needs access to current, private, or domain-specific information. Fine-tuning is more focused on changing model behavior, style, or task-specific output patterns. The appropriate approach depends on the application's requirements.

How is RAG different from a normal LLM?

A normal LLM generates responses primarily from the knowledge and patterns learned during training. A RAG application adds a retrieval step that finds relevant information from an external knowledge source and provides that information to the model before generating the response.

How can I build a RAG application?

A production RAG application typically requires document ingestion, chunking, embeddings, retrieval, context construction, generation, evaluation, and deployment. If you need help building one, see the RAG development services page.

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