Retrieval-Augmented Generation (RAG)

1. Introduction

Retrieval-Augmented Generation (RAG) is an Artificial Intelligence technique that combines information retrieval with Large Language Models (LLMs) to generate more accurate, relevant, and context-specific responses.

Large Language Models such as GPT, Gemini, and Claude are trained on large amounts of data. However, they may not have access to a company's private information, newly created documents, or the latest organizational policies. RAG solves this problem by allowing an AI system to retrieve relevant information from external knowledge sources before generating an answer.

Instead of depending only on the information learned during model training, the LLM receives additional context retrieved from external sources. These sources can include PDF files, Word documents, databases, websites, company documentation, Notion pages, Confluence pages, and other knowledge bases.

For example, if an employee asks an AI chatbot, "What is our company's leave policy?", the system can search the company's HR documents, retrieve the relevant leave policy, and provide an answer based on the actual company information.

RAG is especially useful for enterprise applications because it allows organizations to build AI assistants that can work with their own private and frequently changing information.

2. Objective

The main objective of RAG is to build an AI system that can retrieve relevant information from an external knowledge source and provide that information to a Large Language Model as context for generating an answer.

The system aims to:

• Provide accurate and context-specific answers.

• Use private company documents and data.

• Reduce incorrect or hallucinated responses.

• Access information that may not be present in the LLM's original training data.

• Keep information updated without retraining the entire LLM.

• Build AI assistants for company-specific knowledge.

3. How RAG Works

A typical RAG system works through several stages.

Step 1: User Asks a Question

The process begins when a user submits a question to the AI system.

For example:

What is our company's leave policy?

The question is sent to the RAG system for processing.

Step 2: Retrieve Relevant Information

The system searches an external knowledge source to find information related to the user's question.

The knowledge source may contain:

• PDF documents

• Word documents

• Company manuals

• Databases

• Websites

• FAQs

• Notion pages

• Confluence documentation

• Internal company documentation

The system identifies the documents or sections that are most relevant to the question.

Step 3: Provide Context to the LLM

After retrieving the relevant information, the retrieved content is added to the user's question as additional context.

The LLM receives something similar to:

Question:
What is our company's leave policy?

Relevant Company Documentation:
[Retrieved leave policy information]

Use the provided documentation to answer the question.

The LLM can now generate its response using the retrieved information.

Step 4: Generate the Final Answer

The LLM analyzes the question together with the retrieved documents and generates a response.

The final answer is therefore based on the organization's actual documentation rather than relying only on the model's general knowledge.

4. Example of RAG

Suppose a company has developed an ERP system and wants to create an AI assistant for its employees.

A user asks:

How do I create a purchase order?

Without RAG

Without RAG, the AI may answer based on its general knowledge of ERP systems. However, every ERP system can have different menus, workflows, permissions, and procedures.

The AI might therefore provide an incorrect or generic answer.

With RAG

With RAG, the system searches the company's ERP documentation.

It may find a document such as:

Purchase Order Guide

1. Open the Purchasing module.
2. Select Purchase Orders.
3. Click Create New Purchase Order.
4. Select the supplier.
5. Add the required products.
6. Enter the quantities.
7. Submit the purchase order for approval.

This information is provided to the LLM as context.

The AI can then generate an answer based specifically on the company's ERP system.

Therefore, RAG makes the response more accurate and relevant to the user's actual environment.

5. RAG Architecture

A basic RAG architecture can be represented as follows:

User
   │
   ▼
Question
   │
   ▼
Retriever
(Search relevant documents)
   │
   ▼
Relevant Documents
   │
   ▼
LLM (GPT, Gemini, Claude, etc.)
   │
   ▼
Final Answer

The Retriever is responsible for finding relevant information, while the LLM uses the retrieved information to generate a natural-language response.

6. Document Processing and Embeddings

Before documents can be searched efficiently, they are usually processed and converted into numerical representations called embeddings.

The typical process is:

Documents
    ↓
Text Extraction
    ↓
Text Chunking
    ↓
Embedding Model
    ↓
Vector Embeddings
    ↓
Vector Database

Large documents are usually divided into smaller sections called chunks. Each chunk is converted into an embedding using an embedding model.

An embedding represents the semantic meaning of a piece of text as a numerical vector. Similar pieces of information have similar vector representations.

These vectors are stored in a vector database.

7. Vector Databases

A Vector Database stores and searches numerical embeddings efficiently. When a user asks a question, the question is converted into an embedding and compared with the stored document embeddings.

Common vector database technologies include:

• Pinecone

• ChromaDB

• Qdrant

• Weaviate

• Milvus

• pgvector

For example, if the user asks:

How many days of annual leave do employees receive?

The question is converted into an embedding. The vector database searches for document chunks with similar semantic meaning and retrieves the most relevant sections of the company's HR policy.

8. Complete RAG Workflow

A typical RAG system can be divided into two major processes.

Document Ingestion

The first process prepares the knowledge base.

Documents
    ↓
Extract Text
    ↓
Split into Chunks
    ↓
Generate Embeddings
    ↓
Store in Vector Database

Question Answering

The second process occurs when the user asks a question.

User Question
      ↓
Generate Question Embedding
      ↓
Search Vector Database
      ↓
Retrieve Relevant Chunks
      ↓
Add Context to Prompt
      ↓
Send to LLM
      ↓
Generate Answer

This architecture allows the AI system to access information that is not necessarily part of the LLM's original training data.

9. Why Use RAG?

RAG provides several important advantages.

Uses Your Own Data

Organizations can connect their AI assistant to internal documents, manuals, policies, databases, and knowledge bases.

Reduces Hallucinations

By providing relevant source information to the LLM, RAG can reduce the likelihood of the model generating unsupported or incorrect information.

Keeps Information Updated

When company information changes, the documents in the knowledge base can be updated without retraining the entire language model.

Supports Private Information

RAG can be used to answer questions about private company information that is not publicly available.

Lower Cost Than Fine-Tuning

For many knowledge-based applications, updating a document collection and its embeddings can be significantly simpler and less expensive than retraining or fine-tuning a model.

Context-Specific Answers

The system can provide answers based on the organization's actual documentation instead of generic information.

10. RAG vs Fine-Tuning

11. Advantages of RAG

RAG has several advantages for modern AI applications:

• Provides access to external knowledge.

• Can work with private company information.

• Reduces dependence on the LLM's training data.

• Makes it easier to update knowledge.

• Can improve answer accuracy.

• Supports domain-specific AI assistants.

• Can work with different document formats.

• Can provide answers based on retrieved sources.

• Does not require retraining the entire LLM whenever information changes.

12. Limitations of RAG

Although RAG is powerful, it also has some limitations.

The quality of the final answer depends heavily on the quality of the retrieved documents. If the retriever selects irrelevant or incomplete information, the LLM may still produce an incorrect answer.

Document processing can also be challenging. Poorly formatted PDFs, scanned documents, tables, images, and complex layouts may require additional processing.

Another challenge is selecting the correct chunk size. If chunks are too small, important context may be lost. If they are too large, the retrieved information may contain unnecessary content.

RAG systems also require additional components such as embedding models, vector databases, document processing pipelines, and retrieval systems.

13. Applications of RAG

RAG can be used in many real-world applications, including:

• Company knowledge assistants

• Customer support chatbots

• ERP AI assistants

• HR policy assistants

• Legal document analysis

• Healthcare information systems

• Research assistants

• Technical documentation assistants

• Educational assistants

• Financial document analysis

• Internal company search systems

For example, an ERP application could use RAG to allow employees to ask questions such as:

How do I create a purchase order?
How can I generate a sales report?
What is the process for creating a new customer?
How do I check current inventory?

The AI assistant can retrieve the relevant ERP documentation and provide instructions based on the actual system.

14. Conclusion

Retrieval-Augmented Generation (RAG) is an important AI technique that combines information retrieval with Large Language Models. Instead of relying entirely on the knowledge stored within an LLM, RAG retrieves relevant information from external sources and provides it to the model as additional context.

The basic RAG workflow consists of collecting documents, splitting them into smaller chunks, generating embeddings, storing them in a vector database, retrieving relevant information when a user asks a question, and providing the retrieved information to an LLM.

RAG is particularly useful for enterprise applications because it allows AI systems to work with private, domain-specific, and frequently changing information without requiring the entire language model to be retrained.

Overall, RAG provides a practical foundation for building intelligent applications such as company knowledge assistants, ERP assistants, customer support systems, document analysis tools, and AI-powered search systems.