Explained: What is Retrieval-Augmented Generation (RAG)?

In the Explained series, we break down the key technologies behind Aplysia, HiJiffy’s AI solution tailored for hospitality. You might remember our earlier article about Large Language Models (LLMs), the powerful engines driving modern AI. But LLMs come with limitations. They can’t access new information after training and sometimes get things wrong.

This is where Retrieval-Augmented Generation (RAG) comes in. It powers Aplysia 3, the latest evolution of our AI, and marks a significant step forward in how hotels can use artificial intelligence.

Rather than relying only on static knowledge, RAG helps AI access your hotel’s most relevant content, such as FAQs, guest guides or booking policies, in real time. The result? Fast, accurate and context-aware responses that feel personalised and trustworthy.

Let’s break it down.

• What is RAG? What makes it a smarter approach?
• How vector databases help your AI find the right answer
• RAG components and how they work together
• Benefits of using RAG
• Challenges and Limitations
• Ready to improve every guest interaction with smarter AI?

What is RAG? What makes it a smarter approach?

LLMs have changed the way we interact with technology, powering everything from chatbots to data analysis tools. But they’re not perfect. Once a LLM is trained, its knowledge is locked. It won’t know about your updated policies, seasonal offers or even your latest menu items unless retrained entirely. It also won’t have access to the unique details that matter most to your hotel. And that’s okay. LLMs aren’t built to know everything. They’re designed to understand language, generate content and adapt to context.

Here’s where a smarter approach comes in: Retrieval-Augmented Generation (RAG). Instead of relying only on pre-existing data in the model, RAG has access to fresh, relevant information from your sources. Instead of guessing, the model retrieves exactly what it needs from documents like guest guides, FAQs or internal procedures and uses that content to answer accurately.

For hotels, this is a big deal. Earlier versions of Aplysia used structured prompts to include custom details, and they worked well, up to a point. The challenge? Prompts can only handle a limited amount of text. That’s where vector databases come in. They let the model sift through large amounts of information and pull out exactly what’s needed quickly and effectively.

The result? Clear, accurate answers tailored to your guests and staff, saving time and improving experiences for everyone involved.

How vector databases help your AI find the right answer

Imagine you have a giant hotel manual. Instead of flipping through every page to find the right detail, your AI asks a clever librarian who instantly brings back the most relevant section. That is essentially what a vector database does.

When you upload your hotel’s content, such as policies, FAQs or guest information, the system breaks it into smaller parts and turns each one into a vector. Vectors are just numerical representations that capture the meaning of the content. Similar ideas are placed close together, so when a guest asks a question, the AI knows where to look.

Here is how it works:

• Your documents are split into smaller parts called chunks.
• Each chunk is converted into a vector that captures what it is about.
• When a guest sends a question, the AI also turns that question into a vector.
• It compares the question vector to the stored ones and retrieves the most relevant chunks.
• These chunks are added to the AI’s input, so it can use the right information to generate a reply.
  
  

This way, the AI does not rely on memory or guesswork. It finds the answer based on your actual content and responds with clarity and confidence.

RAG components and how they work together

This part gets a bit more technical, but it’s worth understanding what happens behind the scenes when Aplysia 3 answers a query from a guest or staff member.

RAG stands for:

• Retrieval: Accessing and retrieving information from a knowledge source, like a document or database
• Augmented: Supporting the AI’s generation with additional content or context
• Generation: Creating a response based on the query and the retrieved information
  
  

Here’s what the full workflow looks like:

1. Knowledge base
   We start with the documents provided by the hotel, things like FAQs, policies, menus or guides. This is the information the AI can use to respond.
2. Chunking
   Since documents can be long, we split them into smaller parts called chunks. These chunks are easier to process and fit into the system.
3. Generate embeddings
   Each chunk is turned into a vector, which is just a way of representing its meaning as numbers. This lets the system understand and compare content.
4. Store embeddings
   The vectors are saved in a vector database, along with the original content and metadata (like file name, tags or upload date).
5. User query
   When someone asks a question, the system turns that query into a vector using the same method as before.
6. Retrieve similar chunks
   The query vector is compared with the ones stored in the database. The system picks out the chunks that are most relevant to the question.
7. Re-rank
   Retrieved chunks are scored and reordered to prioritise the ones that best match the query. This ensures the most relevant content is used.
8. Generate response
   These top chunks, combined with the original query, are sent to the language model. The model then creates the final response based on all the information.
   
   

Benefits of using Retrieval-Augmented Generation (RAG)

Adding Retrieval-Augmented Generation to your AI setup brings clear, practical advantages, especially in a hotel environment where accuracy, speed and guest satisfaction matter.

Here are some of the key benefits:

Better accuracy
RAG systems use real content from your hotel, not just what the AI learned during training. This reduces the risk of hallucinations or vague answers and means guests get responses grounded in facts.

More relevant replies
By pulling in the most context-specific information available, RAG helps the AI answer in a way that reflects your actual policies, offers and setup. No more generic responses that miss the mark.

Always up to date
You can make changes to your hotel’s content and see them reflected in the chatbot right away. There is no need for retraining or waiting for updates to take effect.

Easier to understand and audit
Since the system retrieves real documents as part of the process, it is easier to trace where information came from. That makes it more transparent and trustworthy.

Flexible and future-proof
Retrieval and generation are handled separately, so you can update your content or improve the model without having to rebuild everything.

Saves on tokens
Instead of stuffing everything into a single prompt, RAG selects only the most relevant content. This helps reduce the size of each query, which can improve performance and keep costs in check.

Challenges and Limitations

While Retrieval-Augmented Generation addresses some of the biggest issues with traditional language models, it comes with its own set of considerations. Understanding these helps hotel teams get the best out of the system.

Retrieval quality depends on your content
The AI can only find and use what is already there. If your documents are incomplete, inconsistent or unclear, it may pull the wrong details or struggle to answer properly.

Speed can vary
Because RAG involves searching through a database before generating a reply, response times may be slightly longer compared to a simple, one-step chatbot. This is usually minimal, but it’s something to keep in mind for high-traffic situations.

Content structure matters
For the system to work well, your documents should be well-written and easy to split into chunks. Poor formatting or overly complex text can affect how well information is retrieved.

These limitations are manageable and can often be addressed by reviewing content quality and monitoring performance. Aplysia 3 includes tools to help with this, from content visibility to unanswered question tracking, so you always know where improvements can be made.

Ready to improve every guest interaction with smarter AI?

Large Language Models are powerful, but on their own, they fall short in areas that matter most to hotels, like accuracy, context and keeping up with constant changes.

Retrieval-Augmented Generation fills that gap. By pulling real content from your hotel’s own documents, RAG helps your AI give smarter, more reliable answers. It reduces hallucinations, improves guest satisfaction and gives your team more control over what is shared.

With Aplysia 3, this technology is now fully embedded into HiJiffy’s solution. Updates are instant, content is easy to manage, and the chatbot adapts to your hotel’s unique setup, whether you manage one property or fifty.

In short, RAG makes AI more practical, more personal and far more useful in the real world of hospitality.

Sources

This article is based on technical contributions by Vanda Azevedo from HiJiffy’s AI Team.