What is Retrieval Augmented Generation (RAG)?

What is RAG?

Retrieval-augmented generation (RAG) in AI is the process of enhancing large language modes (LLMs) capabilities. LLMs alone can only answer users' questions based on their training data. These machine learning models often have billions of parameters, but even at this size, the issue is that users ask questions that are outside the scope of their training data. From that point, models tend to hallucinate - and come up with information. Another issue is that it is impossible to train models to know everything about everything. The model size would get so big it would be impossible to handle even by the largest enterprises.

This is where RAG comes to help. It enables LLMs to access the company's external and internal knowledge bases and answer questions related to these. All this without having to retrain the base model with this data. This allows businesses to use LLMs with any data and provide responses to users with higher accuracy and relevancy.

In this article, we’ll explore what is RAG, how it works in practice and what is it good for, as well as what issues it has and how to overcome these.

How It Works?

The way it works is simple but powerful. When you type a question into the system, the retriever analyses and matches it with the best documents or data. Augmentation increases the relevancy through various procedures. The generator uses all the information to create a response based on facts, not generating a text based on training data.

Core RAG Components Explained

In simplified form RAG components are the following:

1. The retriever: retrieves information from files or databases. For example, it can look at data through PDF files and embed this data in a three-dimensional space, also known vector database. In this space, information with closer relationships is placed near each other and vice versa.
2. The Augmentation: combines the retrieved information with user requests and optimizing the relevancy using a feedback loop, documentation ranker, and additional context.
3. The generator: writes the response using a large language model, such as GPT 4.0, Claude, or open-source models. Since the data is now structured, LLMs can understand its contextual meaning, and generate more accurate responses.

In reality, there are many more steps, processes, and tools that make up the whole system, which we will cover in our other articles.

Why Do Businesses Even Use RAG in AI?

1. Reduce hallucinations

You probably have used LLMs and found it to come up with answers. RAG mitigates this by matching responses to actual information found in internal or external documents.

2. Use data without training LLMs

LLMs are limited at generating data based on patterns they learn from training data. But with RAG you can use any data without retraining the model. Need the system to specialize in a specific domain? No problem. Change the reference documents retrieval can access. This makes it possible to create expert systems for domain-specific tasks simply and fast.

3. Access real-time information

Lastly, you can connect the retriever to access real-time data. For example, you have information that changes daily, but you need to ask questions and get the up-to-date information back. The retriever can do that. It can find data directly from sources, which the generator can use to give up-to-date responses.

Use cases

This works for a lot of things. For example, if you’re doing customer support, RAG can look up information about past frequently asked questions and give accurate answers to prospects. Or for research, you can use it to summarize the latest papers. E-commerce companies can use it to offer better product recommendations. In healthcare, you can use it to pull up medical guidelines and much more.

Why Traditional RAG Might Not Be Enough Anymore

Here is the thing RAG is not all perfect.

• It can retrieve information from the wrong documents. Because of that it is not trustable for large-scale enterprise use cases.
• It can hallucinate, which means it just makes things up if it can’t find the info. As a result, the generator gets confused and outputs something you can’t trust.
• It can have problems with speed. The retriever and generator have to work together simultaneously and it can take too long to fetch and generate results.

What Could Replace RAG?

Or better yet what could enhance its capabilities and make it trustable?

There’s this new idea called "Graph RAG." Instead of doing retrieval and generation as separate, sequential steps, it ties them together using graph-based knowledge representation. Often through knowledge graphs or semantic networks to make the information retrieval process more accurate.

Scalability and speed are additional features. Traditional RAG often gets too slow, when it has access to a lot of data. It becomes especially problematic with large-scale enterprise data where speed and precision are non-negotiable. Graph rag handles it because of how it organizes data. It's way more efficient.

For example, if you’re working with financial data, complicated medical reports, or other important tasks that require accurate information, graph RAG is the answer. The system retrieves the most relevant data and ensures the context matches your query to provide trustworthy outputs.

Conclusion

With the wave of generative AI, augmented retrieval generation has enabled us to retrieve factual information from files and databases. It’s not just the model generating random text anymore. It’s grounded, meaning it has actual data behind it. This allows us to use LLMs even when their training set is limited.

However, it still has issues such as hallucinations and slow processing time. The solution for this is a graph rag. It enhances traditional RAG capabilities by connecting the retrieval and the generator in a graph-based knowledge representation to provide higher accuracy responses even for complex tasks.