Understanding Retrieval-Augmented Generation (RAG) and How it Works?

The age of Generative AI has made information retrieval a lot easier and faster. While end-users enjoy the perks and advantages of using Generative AI, we don’t see what really goes on behind the scene.

The dataset used for training AI models is finite and limited to those the trainers can access such as public domains, journals, scientific publications, blogs, social media content and so on. Relying on these dataset alone is simply not enough to ensure that the information is recent, reliable and without bias. Therefore, they called in the big guns; Retrieval-Augmented Generation (RAG).

Due to this technology, it is easy to get trusted niche specific or real-world information from the internet with only the help of chatbots.

In this article, we will explore what RAG is and highlight its importance.
Let’s dive in!

What is Retrieval-Augmented Generation?

Retrieval-Augmented Generation (RAG) is an AI framework that uses information retrieval and text-generation to optimize the performance of AI models by connecting them with real-time external knowledge bases. Large Language Models (LLMs) solely rely on the information they were trained on. However, as powerful as they are, they come with certain limitations such as:

Limited knowledge: LLMs can only generate responses based on their training data. The downside of this is that this training data may be outdated or lack domain-specific information.
Generic information: LLMs will only generate vague or imprecise answers if they don’t have access to niche specific information.
Hallucinations: this occurs when LLMs generate plausible sounding but incorrect responses.

Due to these limitations the LLMs can not function optimally. To prevent this, AI developers incorporate RAG architecture which helps AI models generate up-to-date and domain-specific responses.

There are two components of RAG: Information retrieval and text-generation.

• Information retrieval: this allows AI to access additional, real-time information from external sources such as journals, scholarly articles, internal organization data, and specialized dataset.
• Text-generation: this information is then integrated into the response generation process where chatbots use natural language processing (NLP) to interpret and generate the appropriate niche specific response.

How does Retrieval-Augmented Generation work in 6 simple steps

RAG follows specific steps to ensure that it adequately optimizes LLMs. Let’s take a look at 6 simple steps that will help you fully understand how RAG works.

Step 1: Information retrieval

An information retrieval component uses the user input to first pull information from a new data source. But first, you must gather all the data and information needed for the AI model. For example, when developing a customer support chatbot for a manufacturing company, you can include user manuals, a list of FAQs, and a product database.

Step 2: Data chunking

The data you have gathered must now be divided into semantic chunks. Data chunking involves breaking data down into smaller chunks that can be easily read. For instance, if the user manual you upload early is lengthy you might break it down into different sections or headings that each address a particular query.

By doing so, each chunk of data represents a potential response or source of information for the user’s query. This way, the model is more likely to retrieve the relevant information from the data.

In addition, data chunking also improves the efficiency of the model as information is retrieved faster.

Step 3: Data embedding

Each chunk of data is converted into a mathematical representation by a process called embedding. Embedding helps the system semantically understand the text, match the similarities between the data chunks, and match each embedding appropriately with the user query instead of a simple word-to-word comparison.

In addition, embedding also helps the system index the data in vector databases.

Step 4: Retrieval

When a user inputs a query, it must be converted to a vector or an embedding to match the data or document embedding. Ensure to use the same model for both data and query embedding just so the two can be uniform.

Once the query is converted into an embedding, the system matches it to the data embeddings. It then identifies the particular chunks that are most similar to the query embedding.

Step 5: Response generation

The retrieved information gives the LLM more context which provides it with more understanding of the topic. Also, the RAG augments the user’s query by adding the relevant retried information to add more context. Prompt engineering techniques are used to help the RAG communicate effectively with the LLM.

With this augmented prompt and the retrieved information allows the LLM to generate responses that are accurate and contextually relevant to the user’s.

Step 6: Updating external data

The data we use today will be obsolete or stale in the next few years. Therefore, we must always keep the external data fresh with up to date information. To keep the information current for retrieval, asynchronously update the documents and update embedding representation of the documents. There are two ways you can do this. You can either do automated real-time processes or periodic batch processing.

What is the importance of RAG AI?

Information is a very useful tool. However, the wrong information can be a tool of destruction and can even go as far as causing global and economic unrest. This highlights the importance of RAG amongst others, which includes;

1. Higher LLM accuracy and reduced hallucinations: Since RAG retrieves information from verified documents and data, the chances of false motion and hallucinations from an AI model is greatly reduced.
2. Easier access to real-time data: End users can now easily gain access to real-time, and up-to-date data without stress. This allows them to use this data to stay updated on policies, industry changes, and a tool for getting constantly changing knowledge.
3. Enhanced domain customization: Business owners can easily customize their domain with RAG. They can input documents like user manuals and guides, FAQs, operational guidelines and so on. This creates a highly specialized AI model that has multiple functions in regards to the business. Since the AI model becomes a hub of information, it can function as a customer support chatbot, an analyst, or an advisor which understands the business and offers solutions to problems that may arise.
4. Improved user experience: Due to the factual information obtained from RAG AI models, users are rest assured that the information they’re getting is reliable. This causes the interaction between AI and end-users to be smooth and highly reliable l.

Real-world applications for RAG AI

Retrieval-Augmented Generation AI is a valuable system that finds various applications in industries and households. It helps LLMs generate information that is factual and contextually correct. Here are some real-world applications of RAG where accurate information is important;

1. Healthcare
2. Legal
3. Academic research
4. Finance
5. Technical documentation

Conclusion

The main challenge of Generative AI is getting accurate and contextually relevant information in response to a user’s query. RAG helps to solve this problem, and leverages NLP capabilities of LLMs to generate the relevant information. As long as the data is regularly, and asynchronously updated, RAG AI will stay relevant for years to come. In the future, we hope to see more of these developments that enhance the relevance of AI systems.

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