What is Retrieval Augmented Generation?

The past few years have seen a sudden upsurge in language models like BERT, GPT, and T5. Now, a new model known as Retrieval Augmented Generation(RAG) is paving its way. 

RAG has a substantial upper hand compared to models like BERT and GPT. While GPT can generate generic context, RAG takes it one step higher by mixing factual, accurate data. 

Here we will talk about exactly what is RAG. Why it's important for businesses, especially customer support teams, and why you should be using it. Let’s dive in. 

What is Retrieval Augmented Generation? 

Generative AI excels at creating text-based responses based on large language models. However, there's a downside. The answers are often broad context and are limited to information used to train the AI. 

This means any responses that you get will be using weeks, months, or even years-old data. Thus, leading to incorrect responses that can guide customers and erode their trust in this technology. 

This is where RAG filled the gap. RAG became public after the article "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" was published in 2020. It was presented by Patrick Lewis and a team at Facebook AI Research published it.

Now, you know why RAG is needed. Let’s understand what it is. In simple words, Retrieval Augmented Generation is a language that optimizes the language learning models’ output using targeted information. The most significant advantage of RAG is that it does not modify the underlying model. Since the data used is often up-to-date, RAG systems can come up with more accurate and factual responses. 

Benefits of Retrieval Augmented Generation 

Retrieval Augmented Generation (RAG) is a game changer in language technology. RAG combines a retrieval mechanism with large language models (LLMs), bringing several essential benefits to how we use language tools. Let's explore why Retrieval Augmented Generation is so helpful.

1. Enhanced LLM Memory

RAG enhances language models' Long-Short Term Memory (LLM). This approach includes a retrieval mechanism, allowing the model to grab a large amount of information. This enhancement gives the model a better understanding of context. 

Enhanced LLM memory helps businesses with more informed decision-making, personalized customer interactions, and efficient problem-solving.

2. Improved Contextualization

RAG stands out because it makes the model understand context better. It does this by combining the model's creativity with a retrieval system. As a result, the model not only creates content that fits the context but also shines in providing accurate and fitting answers to the questions. 

This better understanding helps businesses connect with customers with more accurate answers and in a more personal way. 

3. Updatable Memory

One of RAG's defining features is its updatable memory system. This innovative mechanism allows for seamless integration of new information into the model's existing knowledge base. 

Unlike static models, RAG continuously updates itself with the latest data, encouraging continuous learning. This adaptability represents a significant advantage in rapidly evolving contexts, enabling businesses to stay current and decision-makers to access real-time insights. 

4. Reduces AI hallucinations

AI-generated content sometimes has minor errors, known as "hallucinations," making it less reliable for essential tasks. Retrieval Augmented Generation (RAG) fixes this by smartly adding new info and reducing mistakes for precise and accurate results. 

RAG's accuracy means dependable content, which is crucial for tasks needing trust. RAG becomes a reliable tool in essential jobs, giving confidence in its accurate results. This helps businesses connect with customers with real-time and accurate data.

5. Reduces Computational And Financial Costs

The RAG model is cost-effective. It reduces the need for a lot of computer power and money by making information processing more efficient. 

Whether in computational power or financial resources, RAG ensures an economical approach. Thus, enabling businesses to allocate resources wisely and achieve operational efficiency without compromising quality.

Applications of Retrieval Augmented Generation

Let's explore all the things that Retrieval Augmented Generation (RAG) can do. RAG has a wide range of uses, from making search engines better to improving tools that summarize information. We'll see how it even helps with support systems and makes various tasks more manageable.

1. Information Retrieval Systems

Retrieval Augmented Generation (RAG) dramatically affects how search engines work. It's like supercharging them.

For example, AptEdge's GPT Answer Engine. When someone asks questions about complicated medical stuff, it doesn't just give a quick answer. 

It uses RAG to explain everything in detail, ensuring the information is accurate and easy to understand. This makes searching for any information, incredibly complex stuff, way better and more helpful.  

2. Conversational Agents and Chatbots

The chatbots—those digital talkers you find on websites. Retrieval Augmented Generation (RAG) makes them more intelligent. The AptEdge GPT-powered chatbot is an excellent example of this. 

With RAG, it's not only about answering questions. It actually enacts the human way of conversation, which feels more like a real conversation. This is super helpful, especially in things like the customer service arena!

It makes users feel understood and happy because the chatbot gives answers that fit their questions.

3. Content Summarization Tools

We are exploring how Retrieval Augmented Generation (RAG) makes summarizing content clever. Picture a tool using RAG to turn big articles into short but super-helpful summaries. 

It's like getting the main points of a long story without missing anything important. RAG makes content summarization tools work better, ensuring you get the most essential info. 

4. Knowledge Base Enrichment

Let's talk about making your knowledge base even better. Retrieval Augmented Generation (RAG) is the hero here. Think of AptEdge's GPT Answer Engine—it's like having a super-smart assistant for your information hub. 

Instead of just adding data, it makes your knowledge base way more helpful. It adds accurate and detailed info that fits the context perfectly. 

It makes your company's information hub smarter every day, ensuring your team always has the most valuable and up-to-date information at their fingertips.

5. Support With Ticketing Systems

The RAG model can be of huge support to customer service teams. It can help you quickly resolve customer tickets by retiring the right information within a second. A superb example of this is AptEdge's Edge Automation. 

Imagine your customers running into a problem and using this tool for support. It doesn't just quickly process your request; it also gives you all the details you need to solve that issue. This makes things easier for the support team as well as ensures you get the help you require quickly and without any hassle.

How Does Retrieval Augmented Generation Work?

Now, let's understand how retrieval augmented generation works. 

Step 1: Data Retrieval 

In this stage, the machine is presented with a prompt or a query. After analyzing the prompt, the RAG model retrieves a set of documents/ passages from an extensive database that is relevant to the query. This is done using a retrieval mechanism based on dense vector representation of query and documentation. 

Step 2: Generation Step

Once data is retrieved, it's time for content generation. In this stage, the data extracted in the first stage is fed to the generative model along with the original query. This generative model generates a response by combining pre-trained knowledge and information from retrieved data.

Step 3: Training

Finally, comes the training stage. The entire system, including both retrieval and generation components, can be improved through training. In this stage, the model improves its retrieval choices based on the quality of generated responses. 

What Challenges Does The Retrieval Augmented Generation Or RAG Solve?

1. Can Be Fine-Tuned For Specific Industries 

One of the most significant disadvantages of generative AI models is their generic answers. However, RAG can be of enormous help here. It can be trained for specific domains using the domain-specific data at the retrieval step. Thus, it enables businesses to generate domain-specific content instead of generalized content. 

2. High accuracy, fewer hallucinations 

AI hallucinations are a big deal as they can often generate wrong data. However, RAG leverages factual and accurate data from the knowledge base and provides highly accurate information. 

When should I use Retrieval Augmented Generation (RAG)?

The actual life usage of RAG is in chatbots. Here's how. Many chatbots are trained for finite intent. They can answer user queries within a small scope. Any question out of that scope will lead to no answers or wrong answers. 

RAG technology helps here. How? 

RAG improves the capacity of current bots by allowing them to answer queries that aren't in the intent list. For example, Generative AI won't be able to handle queries about new products as the data won't be up-to-date, but RAG can.

Since RAG is retrieving up-to-date data from the database, it can answer queries about new products. The same goes for other instances, like cricket fans asking for current cricket updates or hikers asking about current weather updates. 

Conclusion

Retrieval Augmented Generation technology is an evolution of the current language models. It combines the retrieval mechanisms and language model to provide accurate, more prosperous, and detailed outputs. This is good news for customer support service, as RAG can automate information retrieval, but in a more accurate way! At AptEdge, we are helping customer support teams reduce their tickets, and help customers use automated systems that leverage RAG models. 

Check them out here!