What is RAG in Artificial Intelligence: How Chatbots Use Your Information
If you've ever wondered how an artificial intelligence chatbot can answer specific questions about your company, your products or your policies without inventing information, the answer is in three letters: RAG.
RAG (Retrieval-Augmented Generation) is the technology that allows language models like GPT to query real information about your business before generating an answer. Instead of relying solely on what the model "learned" during its training, RAG allows it to search your documents, catalogs and knowledge bases to provide accurate, up-to-date answers.
In this guide we explain what RAG is, how it works step by step, why it is essential for enterprise chatbots and how Aurora Inbox uses this technology to train AI agents with your company's information.
Technical definition of RAG
RAG (Retrieval-Augmented Generation) is an artificial intelligence architecture that combines two fundamental capabilities:
- Retrieval of information: Search and find the most relevant text fragments within your own knowledge base.
- Text generation (Generation): Use a large language model (LLM) to generate a natural and coherent response based on the information retrieved.
In simple terms, RAG is like giving an AI assistant access to your company's library before it answers any questions. Instead of improvising or making up data, the assistant first consults the relevant documents and then formulates its answer based on verifiable information.
RAG vs. LLM without RAG: The Key Difference
| Appearance | LLM without RAG | LLM with RAG |
|---|---|---|
| Source of information | Training knowledge only | Company's own documents + training knowledge |
| Precision | Can invent data (hallucinations) | Answers based on real information |
| Update | Limited to model cutoff date | Updated when new documents are added |
| Customization | Generic for all users | Specific for each business |
| Transparency | It does not indicate where the information comes from | You can cite sources and documents |
| Implementation cost | Only requires access to the model | Requires document indexing + template |
How RAG works: The step-by-step process
To understand how RAG works in an enterprise chatbot, it is important to know its two main phases: the preparation phase (indexing) and the query phase (retrieval + generation).
Diagram of the RAG process
PHASE 1: PREPARATION (to be done only once per document)
=========================================================
Company Documents] [Company Documents] [Company Documents
|
| PDFs, web pages, catalogs, manuals, manuals, etc.
|
v
Word processing] | [Word processing
|
| Text is divided into chunks (chunks)
| Example: 200-500 word paragraphs
|
v
[Embeddings Model]
|
| Each fragment is converted into a numeric vector
| representing its semantic meaning
|
v
[Vector database]
|
| Vectors are stored in a specialized database for similarity search
| specialized for similarity search
|
v
| [Query-ready index] [Query-ready index
PHASE 2: ENQUIRY (occurs in every user message)
=========================================================
[User asks a question]
|
| "How much is the price of the professional plan?"
|
v
[Conversion to Vector] [Conversion to Vector] [Conversion to Vector] [Conversion to Vector
|
| The question is converted to a vector
| with the same embeddings model
|
v
[Similarity search]
|
| The query vector is compared with
| all the vectors in the database
| retrieve the 3-5 most relevant fragments
|
v
[Context construction]
|
| A prompt is assembled that includes:
| - The user's original question
| - The fragments retrieved as context
| - System instructions
|
v
[Language Model (LLM)] - [Language Model (LLM)
|
| The LLM generates a response based ONLY on the information provided.
| ONLY on the information provided
|
v
Precise response to the user | [Precise response to the user]
|
"The professional plan costs $99/month and includes 5 agents and 10,000 messages.
and includes 5 agents and 10,000 messages."
Phase 1: Indexing of documents
The first phase of RAG consists of preparing the knowledge base. This process is performed once for each document and is updated when the information changes.
Collection of documents: It gathers all the materials that the chatbot should know: product manuals, price lists, return policies, frequently asked questions, product catalogs, company web pages, etc.
Fragmentation (Chunking): Documents are broken down into smaller, more manageable fragments. This is crucial because language models are context limited and because smaller chunks allow more precise information to be retrieved. A 50-page document could be divided into 200 chunks of 300 words each.
3. Generation of embeddings: Each text fragment is transformed into a numerical vector (a list of numbers) using an embedding model. These vectors capture the semantic meaning of the text: fragments with similar meanings will have similar vectors, regardless of the exact words used.
4. Vector database storage: The vectors are stored in a specialized database (such as Azure AI Search, Pinecone, or MongoDB with vector search) that allows fast semantic similarity searches.
Phase 2: Recovery and generation
When a user asks a question to the chatbot, the second phase is activated:
1. Vectorization of the query: The user's query is converted into a vector using the same embedding model as in the indexing phase.
Semantic search: A similarity search is performed in the vector database. The system finds the snippets whose meaning is closest to the user's question. For example, if the user asks "how much does premium service cost", the system will retrieve snippets that talk about prices, plans and rates, even if they do not contain the exact words of the question.
3. Context injection: The retrieved fragments are inserted into the prompt that is sent to the language model, along with the original question and system instructions.
4. Response generation: The LLM generates a response using only the information provided in the context. This drastically reduces hallucinations because the model has real data to rely on.
Why RAG is essential for enterprise chatbots
Large language models such as GPT-5 or Claude are remarkably capable of generating coherent text and maintaining natural conversations. However, they have critical limitations when used in business contexts:
The problem of hallucinations
Without RAG, an LLM can make up information that sounds convincing but is completely false. If a client asks "what is your return policy", an LLM without access to your documents could generate a policy that sounds reasonable but does not correspond to the reality of your company. This can cause legal problems, loss of trust and customer confusion.
The problem of out-of-date
Language models have a "cut-off date" of knowledge. They don't know about recent changes in your pricing, new products, or policy updates. RAG solves this because the knowledge base can be updated at any time without retraining the model.
The problem of generalization
Without RAG, a chatbot will give generic answers applicable to any company in the industry. With RAG, the chatbot responds with information specific to your business: your prices, your hours, your products, your policies.
Concrete benefits of RAG for companies
- Precision: The answers are based on verifiable information from your company.
- Trust: Customers receive correct data, not fabricated data
- Immediate update: You change a document and the chatbot reflects the changes.
- Scalability: You can add hundreds of documents without retraining models
- Cost reduction: Fewer errors, fewer escalations to human agents
- Traceability: You can identify which document each answer came from
Practical applications of RAG in WhatsApp chatbots
RAG is not just a theoretical technology. It has practical and immediate applications for companies that use chatbots in channels such as WhatsApp:
Customer Support
A RAG-enabled chatbot can answer questions about warranties, return policies, technical troubleshooting steps and order status, all based on the company's actual documentation. If a customer asks "how do I return a product," the chatbot consults the updated return policy and gives precise instructions.
Sales and product catalog
The chatbot can answer detailed questions about products: technical specifications, availability, prices, comparisons between models. All the information comes from the company's real catalog, not from generic data invented by the model.
Appointment scheduling
Combined with a calendar plugin, a RAG-enabled chatbot can inform about available services, durations, prerequisites and costs before scheduling an appointment. The information about the services comes from the company's knowledge base.
Employee onboarding
An internal chatbot with RAG can answer questions from new employees about internal processes, company policies, benefits and procedures, based on HR manuals and documents.
Education and training
Educational institutions can create chatbots to answer questions about academic programs, admission requirements, calendars and administrative processes, using the institution's official documentation.
How Aurora Inbox implements RAG
Aurora Inbox uses an advanced implementation of RAG to allow companies to train their AI agents with proprietary information. The system is designed to be accessible and requires no technical knowledge to configure.
Sources of knowledge supported
Aurora Inbox allows feeding the AI agent's knowledge base with multiple types of sources:
- PDF documents: Manuals, catalogs, price lists, policies, contracts. The system extracts the text, fragments it and indexes it automatically.
- Web pages (URLs): The agent can crawl company web pages to extract updated information. Ideal for e-commerce sites, service pages or informative blogs.
- Product catalogs: Direct integration with the company's product catalog, including names, descriptions, prices, variants and availability.
- Personalized text: Information written directly on the platform, such as answers to frequently asked questions, sales scripts or specific instructions.
Technical architecture of RAG in Aurora Inbox
Aurora Inbox implements RAG using a robust and scalable architecture:
-
Document processing: Uploaded documents are processed by a dedicated service that extracts text, handles different formats and divides the content into optimized fragments.
-
Embeddings and vector search: Azure OpenAI embedding models are used to convert fragments into vectors. The search is performed using Azure AI Search, which allows hybrid search (semantic + keyword) for higher accuracy.
-
Orchestration with Semantic Kernel: The Aurora Inbox agent system is built on top of Microsoft Semantic Kernel and Agent Framework, which allows combining RAG with other plugins such as scheduling, product catalog and transfer to humans in the same conversation.
-
Contextual responses: When a customer sends a message via WhatsApp, the AI agent searches the knowledge base, retrieves the relevant snippets and generates an accurate and natural response, all in less than 3 seconds.
Practical example with Aurora Inbox
Imagine a dental practice setting up its AI agent in Aurora Inbox:
- Upload a PDF with its list of services and prices
- Add URL of your web site with information about the doctors
- Write custom text with cancellation and rescheduling policies
When a patient writes via WhatsApp: "How much does a dental cleaning cost and what does it include?", the agent:
- Search the knowledge base for "teeth cleaning" and "prices".
- Retrieves information from the service PDF
- Generate a response: "The dental cleaning at our clinic costs $45 and includes general check-up, ultrasonic cleaning, polishing and fluoride application. The procedure takes approximately 45 minutes. Would you like to schedule an appointment?"
All this happens automatically, 24/7, with real information from the clinic.
Differences between RAG and Fine-Tuning
It is common to confuse RAG with fine-tuning, another technique for customizing AI models. Here are the key differences:
| Feature | RAG | Fine-Tuning |
|---|---|---|
| How it works | Look for information in documents when answering | Modify the internal parameters of the model with training data. |
| Update | Instant: add or modify documents | Requires retraining of the model (hours/days) |
| Cost | Low: storage and search only | High: requires GPU and training time |
| Factual accuracy | High: answers based on specific documents | Media: can mix information |
| Best for | Frequently changing information | Communication style or behaviors |
| Risk of hallucination | Under | Medium-high |
In practice, the best implementations combine both techniques: fine-tuning for tone and style of communication, and RAG for factual and up-to-date information.
Limitations of RAG that you should be aware of
Although RAG is a powerful technology, it is important to be aware of its limitations:
- Quality of documents: If the source information contains errors, the chatbot will reproduce those errors. The quality of the answers depends directly on the quality of the indexed documents.
- Inadequate fragmentation: If documents are fragmented incorrectly, the system may retrieve incomplete or out-of-context information.
- Questions out of scope: If a user asks something that is not in the knowledge base, the system must be configured to recognize this limitation and escalate appropriately.
- Latency: Vector search adds an additional step before generation, which may slightly increase the response time (although in well-optimized systems like Aurora Inbox this is barely noticeable).
The future of RAG in enterprise chatbots
RAG continues to evolve rapidly. Some trends we are seeing in 2025:
- Multimodal RAG: Ability to index and retrieve not only text, but also images, tables and diagrams from documents.
- Agentive RAG: Agents that dynamically decide when to use RAG, when to consult external APIs and when to use their knowledge base.
- RAG with memory: Systems that remember previous interactions of the same customer to further personalize responses.
- RAG in real time: Instant indexing of new documents with no waiting time.
Aurora Inbox is at the forefront of these trends, continually implementing improvements to its RAG system to deliver the most accurate and natural experience possible to companies and their customers.
Frequently asked questions about RAG in artificial intelligence
1. What does RAG mean in artificial intelligence?
RAG stands for Retrieval-Augmented Generation. It is an artificial intelligence technique that allows language models (such as GPT) to consult external documents and knowledge bases before generating an answer, instead of relying solely on their training knowledge. This results in more accurate answers based on real information.
2. What is the difference between RAG and a traditional chatbot?
A traditional rule-based chatbot responds with predefined texts based on detected keywords. A chatbot with RAG understands the user's intent, searches a knowledge base for the most relevant pieces of information and generates a natural and personalized response. The main difference is that RAG combines intelligent information search with the ability to generate coherent text, while a traditional chatbot can only return pre-written answers.
3. Is it safe to use RAG with my company's confidential information?
Yes, as long as the implementation is adequate. In platforms such as Aurora Inbox, each company's documents are stored in isolation and are only accessible by the AI agents of that specific company. The information is not shared between tenants (customers) or used to train general models. It is important to verify that the provider you choose complies with data security and privacy standards.
4. How many documents can I use with RAG in a chatbot?
There is no theoretical limit to the number of documents you can index with RAG. In practice, platforms such as Aurora Inbox allow you to upload multiple PDFs, add multiple URLs and set up extensive product catalogs. The vector search system is designed to scale efficiently, maintaining fast response times even with large knowledge bases. The important thing is that documents are well structured and contain clear information.
5. How long does it take to configure a chatbot with RAG?
With modern platforms like Aurora Inbox, setting up an AI agent with RAG can be completed in minutes. The typical process is: upload your documents (PDFs, URLs or text), wait for the system to process and index them (usually seconds to a few minutes depending on volume), and the agent will be ready to answer questions based on your information. No programming knowledge or advanced technical expertise is required.
RAG is the technology that makes artificial intelligence chatbots really useful for businesses: accurate, up-to-date answers based on real information about your business. If you want to implement an AI agent that knows your company as well as your best employee, Aurora Inbox allows you to do it without technical complications, directly on WhatsApp and other messaging channels.
