Problem Statement
Before explaining what RAG is, let me first address the problem statement and why RAG is needed.
Traditional Large Language Models (LLMs) have the ability to generate human-like text and content. However, they have a significant limitation in their knowledge base. These models are trained on a specific dataset, and once trained, they do not have access to any up-to-date or real-time information. As a result, LLMs are unable to answer user queries about new events or recent developments, since their knowledge is "cut off" at a certain point. This is often referred to as the model’s knowledge cutoff.
To add to this, LLMs sometimes hallucinate when answering queries. This means they may generate information that is factually incorrect or entirely fabricated. These hallucinations occur due to gaps in the model’s knowledge, especially in cases where it is asked about niche topics or details not present in its training data.
Moreover, LLMs also struggle to access domain-specific knowledge such as scientific research, medical information, or legal texts.
Why RAG is Needed?
Given these limitations, a solution was needed to pass up-to-date and highly specific data to LLMs so that they could generate more accurate and reliable responses. This is where Retrieval-Augmented Generation (RAG) comes in.
Introduction
RAG stands for Retrieval-Augmented Generation. In a RAG application, along with passing the user's prompt to the LLM, a chunk of relevant data is also passed. This additional data is retrieved from an external source, based on the user's query. By combining the LLM's generative capabilities with this external information, the model is better equipped to produce accurate responses.
The user’s prompt and the retrieved data are combined into a prompt template, which contains instructions for the LLM on how to handle the data, the user’s question, and the expected format of the response.
How RAG Works
In RAG, data is stored in a vector database instead of traditional SQL/NoSQL databases. When a user submits a query, the system retrieves relevant data from this database. This additional information is then passed to the LLM to generate a more accurate response.
Final Words
RAG is an AI framework that improves the capabilities of LLMs, ensuring that users receive accurate and up-to-date responses, even in specialized domains. To understand the pros and cons of RAG application, check out this blog.
I am currently working on a RAG-based application, and you can check it out in the following video.
Citation
I would like to acknowledge that I took help from ChatGPT to structure my blog and simplify content.
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