Enterprise RAG Architectures (Step-by-Step)

If you ask artificial intelligence about your company’s internal policy, which it has no idea about, it answers with fabrications. Large language models are trained on general information from the internet and do not have access to your internal, private data. This leads to errors, often referred to as "hallucinations".

Retrieval Augmented Generation solves this problem by turning ordinary AI into a reliable corporate tool. Working with tens of thousands of documents and various data sources, this technique adds internal information to the user's query before the LLM generates the answer.

That is why large companies build complex RAG pipelines for typical scenarios, such as searching for information across thousands of internal documents, automating customer support, and analyzing internal company knowledge. This is a bridge that allows the model to use internal details reliably.

Quick Take

• Long documents are broken into small "chunks", each containing a complete idea.
• Each "chunk" of text is converted into a numerical vector that captures its semantic content.
• RAG pipelines include automatic monitoring of changes and reindexing of data as soon as they are updated in the sources.
• At the corporate level, multiple vector databases, distributed pipelines, and GPU clusters are used to ensure reliability and low latency.
• LLM Caching and Distillation are used to reduce costs.

What Data Is Needed for RAG and How to Prepare It

The RAG system works only with the data we provide it. Therefore, the very first and most important step is to prepare internal corporate information. In a large company, data is stored in various locations, including documents in Confluence or SharePoint, files in Google Drive, records in SQL databases, and even archived emails and PDF files.

The RAG system must be able to "ingest" and process all these formats for quality enterprise knowledge retrieval. So, first, the data needs to be cleaned and structured. Unnecessary information, outdated data, or errors can confuse the AI.

Breaking Down into "Chunks"

When AI searches for an answer, it does not need the entire large document. It only requires specific, small text fragments. The chunking process divides long documents into such "chunks", each containing one complete idea or a small piece of information.

We can break the text down by paragraphs, by document structure, or even by semantics, that is, by content, so that the "chunk" does not break an important idea.

The Importance of Metadata

For AI to search more effectively, each "chunk" of data must have its "passport", which is called metadata. Information about who the author is, when the document was created, and what type of document it is is added to each text fragment.

This helps the RAG pipelines system filter search results. For example, if you ask about a policy, AI will only search among documents that have the metadata "Document Type: Policy" and ignore old drafts.

Building the RAG Pipeline

The process of converting raw corporate documents into "smart" data for AI is called the content extraction and update pipeline. This is a step-by-step mechanism that ensures the RAG system always works with high-quality and up-to-date information.

Step 1. Connection and Parsing

First, the pipeline must connect to the company's data sources. These can be servers, cloud storage, or internal databases. Since documents are stored in different formats, parsing occurs to extract pure text from each file type. At this stage, the system learns to ignore images, page headers, and other elements that do not carry textual content.

Step 2. Cleaning and Structuring

The resulting text often contains "junk" information: unnecessary characters, repetitions, or outdated data. This is called noise cleaning. After cleaning, the text is broken down into "chunks". Large documents, such as manuals, are divided into smaller, more manageable fragments that are easier to search and fit better into the model's memory. Each such "chunk" is also enriched with metadata.

Step 3. Creating Embeddings and Storage

Each small "chunk" of text, which already contains metadata, is converted into an embedding, a long array of numbers. This vector captures the semantic content of the text. This is like a document's "fingerprint".

The created vectors are written into a vector database. This is a specialized storage optimized for searching for similarity. That is, when the user then asks a question, the vector database instantly finds the "chunks" of text that have the most significant similarity to the query.

Automatic Data Update

For large companies, AI mustn't provide outdated information. Therefore, the RAG architecture must include automatic update mechanisms:

• Change Monitoring. The system constantly monitors data sources. As soon as a document in SharePoint or Confluence is changed, the system records the update.
• Reindexing. The updated document is automatically processed through the entire pipeline again: the old "chunk" of text is deleted from the vector database, a new embedding is created from the revised text, and the new vector is written.
• Scheduling. In addition to monitoring, a regular reindexing schedule can be set up to ensure that all data remains current.

Response Generation and Quality Control

After the RAG system has found the most relevant "chunks" of corporate information, the response generation stage begins. Here, the LLM combines its basic intelligence with the found context to give an accurate, current, and safe answer.

Query Formulation

For the LLM to know how to use the found context, we use prompt templates.

These are standardized text structures that have three main parts:

• Instruction. Clearly tells the LLM what to do. For example: "You are an HR expert. Use only the provided context to answer the question. Do not fabricate information".
• Context. These are the inserted text fragments that the RAG system found in the vector database.
• User Query. The original question asked by the user.

The template ensures that the LLM does not deviate from the topic and uses only the provided corporate knowledge.

Controlling "Hallucinations" and Response Format

If AI fabricates a legal fact, a financial indicator, or a secret strategy, it can result in significant losses or legal issues. The RAG architecture aims to reduce hallucinations by forcing the LLM to refer to factual data from the context.

For better integration with business processes, the LLM response must be structured data.

• JSON. This is a format easily read by computer programs. It is useful for automatically filling out forms or transferring data to other systems.
• Bullet points. Used to quickly and clearly present key conclusions or a list of steps.
• Quotes from Documents. The LLM must not just retell the information but indicate the source, ensuring transparency and trust.

Answer Verification Mechanisms in the Enterprise

After the LLM has generated a response, additional "safeguards" are activated in the corporate RAG architecture to check its quality.

This is primarily a check that all information generated by the LLM is indeed contained in the provided contextual fragments. If the LLM adds something of its own, the system records it as a potential "hallucination". The system also evaluates the overall quality of the response and the relevance of the found context. This may include assessing the accuracy of the search and the factual compliance of the answer.

And additional software layers check the response for compliance with internal policies. For example, a filter can block a response if it contains profanity, personal data, or forbidden terms, even if they were accidentally generated.

Scaling and Real Architectures

A RAG architecture that works great for a small team with a dozen documents requires serious changes when implemented by corporate giants working with millions of documents and thousands of users.

Components That Change During Scaling

When moving to the enterprise level, the main components of the RAG system require significant reinforcement:

• Multiple Vector Databases. Instead of one database, data is distributed among several vector databases. This is necessary for load balancing and redundancy. If one database fails, the other continues to service requests, guaranteeing uninterrupted enterprise knowledge retrieval.
• Distributed Ingestion Pipelines. The pipeline that processes data and creates embeddings becomes distributed. This allows simultaneous processing of large volumes of new documents and quick reaction to changes, which is critical for data currency.
• GPU Cluster for LLM. To quickly service thousands of requests simultaneously, the LLM is moved to a powerful GPU cluster. This ensures low latency and high throughput.
• Caching Layer. A special caching layer is added. If users frequently ask the same question, the system stores the ready answer in the cache. This allows for an instant response without engaging expensive LLM and vector database resources.

Optimizing LLM Costs

Operating large language models is very expensive. Corporate teams use several methods to optimize costs:

• LLM Caching. Storing responses to repeated queries. This is the simplest and most effective way to reduce the number of expensive calls to the LLM.
• Distillation. Creating a small, "student" model that is trained on the results of a large, expensive "teacher" model. The small model works faster and cheaply while maintaining high accuracy.
• Model Compression. Techniques that reduce the size of the LLM, allowing it to work on less powerful equipment and lowering the cost of GPU clusters.

FAQ

What is a data "chunk" and why break up large documents?

A "chunk" is a small, meaningful text fragment into which a large document is broken down. This is done because AI will find a specific "chunk" of information faster than searching the entire document. Also, each "chunk" fits more easily into the LLM's memory for response generation.

What is an embedding, and why is it stored in a vector database?

An embedding is a numerical vector that is the "fingerprint" of the text, capturing its semantic content. It is stored in a vector database because this specialized storage allows for instant searching of "chunks" of text that are most similar in meaning to the user's query, unlike a regular database.

How does RAG ensure its information is current?

The corporate RAG architecture uses automatic updating. It constantly monitors for changes in the sources. As soon as a document changes, the system automatically triggers reindexing: old vectors are deleted, and new ones are created and written to the database.

How does RAG control "hallucinations" at the response generation stage?

Control occurs through prompt templates. The system clearly instructs the LLM: use only the provided context and do not fabricate new information. Additionally, grounding mechanisms are employed to verify that all generated information is indeed contained within the identified fragments.

Why does the corporate RAG architecture need metadata?

Metadata is the "passport" of each data "chunk". It is necessary for filtering search results. If the user inquires about "new policy," metadata helps AI filter out old drafts and identify only documents tagged "Type: Policy" and with a current date.