What is RAG? A guide to connecting AI with organizational documents and knowledge

What is RAG?

Language models can answer questions across a wide range of topics, but they usually do not contain an organization’s private and current information in their parameters.

A model does not know about a company’s new contract, the latest HR procedure, an internal system or recent changes to a knowledge base unless that information is made available to it.

Retrieval-Augmented Generation, or RAG, is one of the main patterns for addressing this gap.

With RAG, the system finds relevant information from one or more defined sources before generating an answer, then provides that information as Context to the language model.

The model can therefore answer from current or proprietary organizational knowledge without retraining for every information change. This is one important layer in enterprise AI solutions.

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How does RAG work?

A simple RAG architecture usually has two main parts: information preparation and answering.

Information preparation

Documents and data must be collected, processed and made searchable. This can include file ingestion, text extraction, document-structure detection, Chunking, Embedding, Metadata storage and Indexing.

Answering

When a user asks a question, the question is analyzed, relevant information is searched, the most relevant content is selected, Context is provided to the model, and the answer is generated. If supported, the sources can also be shown.

1. Analyze the user’s question
2. Search for relevant information
3. Select the most relevant content
4. Add Context to the model input
5. Generate an answer and, where possible, show its source

Google Cloud also separates RAG into data preparation and serving in its reference architecture.

Core components of a RAG architecture

RAG is not a single capability or merely a vector database. It is a connected chain of document ingestion and preparation, indexing, query understanding, retrieval, ranking, answer generation and access control.

Output quality depends on the weakest part of this chain. Even a strong language model cannot provide a dependable answer when it receives incomplete or irrelevant Context.

What are Embeddings?

An Embedding is a numerical representation of content meaning. Instead of relying only on exact word matches, Embeddings help a system find semantic similarity between a question and different parts of a document.

For example, “What are the employee leave conditions?” and “How many annual leave days do employees have?” do not use exactly the same words, but they are close in meaning.

This is one reason Vector Search and Semantic Search are used in many RAG architectures.

What is Chunking and why does it matter?

A long document is usually not placed into a Prompt in full. It is split into smaller sections, or Chunks, so the system can find the most relevant parts.

Chunking is not simply cutting text every few hundred characters. For organizational documents, the real structure—title, chapter, clause, article, note, table, contract section and procedure—should often be considered.

A Chunk that is too small may lack enough Context, while a Chunk that is too large may add too much unrelated information. Chunking is therefore an important factor in RAG quality.

Retrieval is more than Vector Search

RAG does not necessarily mean using Vector Search alone. Depending on the content, a combination of Keyword Search, Semantic Search, Vector Search, Metadata Filtering, Hybrid Search and Ranking can be used.

Metadata is especially important in an organization. Department, document type, date, version, owner and confidentiality level can all improve Retrieval and narrow the search scope.

Grounded responses

An important enterprise use of RAG is connecting an answer to real sources. Instead of only saying that a request is allowed under organizational policy, the system should ideally identify the document, clause or section behind the answer.

This makes the answer reviewable. A Citation alone, however, does not guarantee correctness.

RAG does not eliminate model errors. A system may retrieve an irrelevant or outdated document, miss the right Chunk, misinterpret Context, receive an ambiguous question or find no valid source. RAG can reduce the likelihood of unsupported answers by providing relevant Context, but a Production system still needs Evaluation.

• Evaluate Retrieval.
• Remove invalid or outdated sources.
• Control the model’s answer.
• Provide human review for sensitive use cases.

Security and access control in RAG

In an organizational system, having a document in a Knowledge Base does not mean that every user should see it. Retrieval must be permission-aware.

If a user cannot access a finance or HR document, the model must not retrieve that content for the same user’s answer.

Identity, Authorization, Role-Based Access, Metadata Filters, Tenant Isolation, Audit and Logging should be considered from the beginning of the architecture.

RAG or Fine-tuning?

These are not direct substitutes. RAG is mainly suited to providing new or proprietary knowledge, such as contracts, procedures, product information, documentation and internal knowledge.

Fine-tuning is more useful when the goal is to change a model’s behavior, response format or specialized skill, such as a specific response style, output structure, classification task or repeatable specialized behavior.

Many systems may use both approaches together.

Enterprise RAG use cases

Knowledge management

Ask questions about organizational documents and procedures.

Support

Search a Knowledge Base and product information for answers.

Human resources

Answer questions about internal policies and processes.

Contracts

Search and extract information from approved contracts.

IT

Search documentation and runbooks.

Operations

Quickly access procedures and process-related information.

RAG at Aivan

At Aivan, RAG can be used as a layer that connects AI models to approved organizational documents and knowledge. In this architecture, Retrieval should be designed together with access control, source management, answer evaluation and source visibility.

This approach works alongside other enterprise AI components and should be implemented according to each organization’s data, workflows and requirements.

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