What is the Big Deal About Retrieval Augmented Generation (RAG)?


Artificial Intelligence technology has taken a huge step forward with the introduction of large language models. These are powerful tools that can understand and generate human language with precision. Many experts expect that the market size will grow tremendously in the next decade as more businesses find innovative ways to incorporate them into their service offerings.

It is true that LLMs’ capabilities are already impressive and there is always room for improvement. Current models have a tendency to hallucinate or present false information as facts, as demonstrated in a special report on ‘AI hallucinations: The 3% problem no one can fix slows the AI juggernaut’. In a response, ChatGPT detailed how Mahatma Gandhi used Gmail to organize meetings. The reality is that LLMs can only understand and generate information based on their training data and cannot access or remember specific details outside of it. This leads to factual inaccuracies. 

Now, this is where Retrieval-Augmented Generation (RAG) helps. It is a framework that means to improve on this limitation. With RAG implementation, LLMs will only become more powerful in the years to come.

Here’s why there’s a big deal about RAG.

What is Retrieval-Augmented Generation?

What is the Big Deal About Retrieval Augmented Generation (RAG)?

Retrieval-augmented generation is a term coined by researchers at Facebook AI. Their research paper on ‘Retrieval-augmented Generation for Knowledge-Intensive NLP Tasks’ describes RAG as a technique that combines the best of retrieval-based and generative AI systems. 

It allows LLMs to access an external knowledge source or database during their operations. They do not depend solely on a pre-trained model as RAG grants LLMs the ability to ‘look things up’. So it pulls relevant information from these external sources as needed. This feature allows the models to incorporate more accurate and fact-checked data into their responses. This is how significantly we can reduce hallucination.

How Does Retrieval-Augmented Generation Work?

An extensive article by MongoDB on ‘What is Retrieval Augmented Generation?’ breaks down the building blocks of RAG architecture.

  • Large Language Model (LLM): The process starts with a large language model. This is a type of AI that has been trained on a vast amount of text. Because of this training, it understands how words and sentences are typically used and knows a lot about common facts that many people know.
  • Vector Embeddings: Vector embeddings are a way to turn words, phrases, or other types of information into a form that a computer can understand. Let me explain in the most simplest way possible. Vector embeddings place each word or phrase in a specific spot in a computer’s memory space. Words or phrases that are similar in meaning are placed closer together. This makes it easier for the AI to find and use them.
  • Orchestration: This is where the magic of RAG really happens. Once the AI has a question or needs more information, it uses vector embeddings to find the most relevant texts or data it has been trained on. After finding the right information, the AI combines what it knows from its training with this newly retrieved data to create a more accurate and relevant response. This step is crucial because it helps the AI avoid making up answers (a problem known as “hallucination”) by ensuring it has real data to back up its responses.

Potential Applications of Retrieval-Augmented Generation

RAG’s use cases are plenty, particularly in fields that rely on information retrieval and content creation. Here are some examples.


Chatbots are rising fast. It is also expected that it may take many chat jobs as most of the problems can be solved by the AI bot. But the significant challenge for this AI is ensuring accurate responses and delivering them in a conversational manner. A chatbot cannot hallucinate and give a solution that does not exist. Here RAG can be used so that chatbots will not only be able to communicate more naturally with humans but will also be more reliable sources of information.

Automated Journalism

You know right how easy it is to spread fake news. If a LLM spreads fake news, then it cannot build trust between itself and the audience. That is why, automated journalism is an essential area where accurate reporting and generation of news articles is crucial to prevent the propagation of misinformation. AI systems could crawl through vast collection of documents and data to generate accurate, well-informed articles, briefings, as well as reports. The best part is that it can even potentially uncover insights from data that human journalists might miss.


Search Engine Optimization

It is important for Search Engine to understand the intent and context of the user to give them the data they want. Search engines can leverage RAG to provide more accurate search results. Instead of relying only on algorithmic interpretations of what a user’s queries might signify, it could pull from vast databases of information. It can then deliver highly accurate responses which will improve the user experience and reduce spam.

AI-Based Learning Systems

In our previous post on ‘Why Does AI Matter in Education?’, we mentioned how AI is changing the academic field. As AI-based learning platforms are becoming more common, integrating RAG into them could provide learners with more accurate and reliable information. Students will also trust the system if there is no wrong information.

Personal Assistants

RAG can make personal assistants like Siri and Alexa more efficient and reliable. Similar to what was discussed in ‘How AI Avatars are Changing Conversation Intelligence’, these AI systems would not only provide answers based on pre-trained models but also pull from an extensive database to generate more precise responses. Like, they can crawl the web and combine the latest information with their pre-trained data to provide you the best information.

The Bottom Line

So, the deal with retrieval-augmented generation is just about enhancing the reliability and usefulness of AI systems. The ability of these systems to access and use specific and accurate information from external data sources will definitely be integrated with AI. It will stand as a significant advancement in mitigating false information generated by AI. 

But it should also be taken care to cite or link back to the sources from which it is picking the information

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