What is RAG?

Time to read: 4 minutes

Date: April 8, 2024

If you’ve been reading about AI at any point in the last year, you’ve probably come across a new term that has been gaining steam recently: RAG. What’s the deal with RAG? What does it mean and where does it come from? The biggest question is likely to be “why does it matter?”. 

Being that Lobby is the OG when it comes to RAG, it seems like a good time to talk about this new acronym, what it means for AI, and why Lobby is miles ahead of the competition.

What is RAG?

The term RAG stands for Retrieval-Augmented Generation. In simple terms, RAG is a type of framework that pulls information from external resources to help LLMs have up-to-date information. It’s easier to understand when you break the term itself down. The generation of the LLM is augmented by retrieving already existing information from sources. This makes it much easier for the model using RAG to have context for the output that it generates. Until recently, many LLMs have not had the ability to supplement their information with additional sources outside of their training material. RAG changes that and has some other big benefits. 

There are two really important things to know about RAG. One is that with RAG, bots are able to have up-to-date information that can be checked against the information it already has. This helps to address the issue of inaccurate or just flat out wrong information that is given to the user. RAG helps improve the accuracy of the answers generated by bots by having them check different current sources of information to improve the answers they generate.

The second benefit that RAG provides is that you can actually check what sources the bot is using to verify the sources and make sure that the information is accurate and trustworthy. This means that when you want to be sure that the bot has the correct sources to pull information from, you can metaphorically open the hood and see what it is working with to generate outputs. 

As a bit of a bonus, RAG also decreases the need for the bot to be repeatedly trained on a model or with new information. This is a big benefit when it comes to the costs and potential time it could take to train a new model effectively. Being able to reduce the amount of time and resources to do this can be very appealing to businesses and those looking to use AI for the long haul.

Need more info? Then let’s look at a brief rundown of how RAG works.

  1. Ingestion: You give the bot sources of information to pull from. 
  2. Input: You (the user) provide a query or prompt to the model that uses RAG.
  3. Retrieval: The retriever looks through external knowledge sources (these can be documents, a database, etc) to find relevant information based on your input query or prompt. 
  4. Generation: A sequence-to-sequence model takes your input and retrieves the relevant information and generates a response. This sequence-to-sequence model is trained on a large dataset of query response pairs. This gives it the ability to learn to give you responses with context and accuracy.   
  5. Output: The model using RAG provides the generated response. The response itself is made up of relevant information that was retrieved from the sources the model used and the input query or prompt. 

One big way that Lobby Studio  stands out in a sea of AI tools is the power it gives its users with RAG. Any bot that you create with Lobby Studio will be able to give you more accurate answers because you are able to give it the data sources it pulls from. This means you have more control over the context of a bots output and the accuracy of the answers it provides.  

How is Lobby Ahead When it Comes to RAG?

As we mentioned above, Lobby has been working with RAG for quite some time now. Lobby built the infrastructure for RAG six months ago and is currently optimizing it. For context, many companies have only just started using RAG for models, but Lobby was at that stage in early 2023. Lobby is ahead of the competition because, rather than being in the early stages of figuring out how to implement RAG for models, it’s already well beyond that stage and is now working towards perfecting it. Lobby has its own proprietary system that handles the augmentation and is able to provide more accurate and reliable outputs than other models.


While other companies are only just beginning to work with and employ RAG when it comes to their AI models and, ultimately, products, this is old-hat to Lobby. At this point, Lobby doesn’t just use RAG, it excels at it. The best news though? Lobby is just going to keep getting better. Using Lobby Studio puts you in control of an AI that is already ahead of the others in terms of RAG, meaning that you can control what resources it pulls from while receiving more accurate and consistent output. 

So what are you waiting for? Get started with the AI that gives you more control, better answers, and more privacy today! Check out Lobby Studio here!