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Enhancing the Power of LLMs with Retrieval Augmented Generation

Apr 2, 2024 | Blogs

Enhancing the Power of LLMs with Retrieval Augmented Generation (RAG)

Retrieval Augmented Generation (RAG) represents a cutting-edge technique in the world of artificial intelligence, specifically in the realm of natural language processing (NLP). It is a framework that synergizes two primary components: the power of a pre-trained language model with the vast, informative potential of a retrievable knowledge base.

Before producing a response, it consults a reliable knowledge base that is separate from its training data sources. 

Large Language Models (LLMs) produce unique output for tasks like question answering, language translation, and sentence completion by using billions of parameters and enormous amounts of data during training. Without requiring the model to be retrained, RAG expands the already potent capabilities of LLMs to particular domains or the internal knowledge base of an organization.

RAG vs Traditional LLMs

Why RAG is helpful?

As stated earlier, LLMs are not always up to date and may not have enough context to answer a user’s query. Additionally, RAG provides users the ability to generate answers from internal/proprietary datasets without sharing the same with any LLM providers. Additionally, it can enhance context for better response generation. 

An example of the same would be a Learning and Training Agent that leverages the internal LMS (Learning Management System) data and generates output based on this dataset as a first choice. In the absence of relevant information, the agent retrieves results from LLM.

RAG leverages vector spaces by first encoding large corpora of text (input data) into dense vector representations. When presented with a query, RAG retrieves the most relevant documents from this corpus by performing a vector space similarity search. These retrieved documents are then used as augmented context, enhancing the generative model’s ability to produce accurate and contextually rich responses. This methodology allows RAG to effectively bridge the gap between the vast knowledge encoded in pre-trained models and the specific information contained within targeted documents, thereby improving the quality and relevance of the generated text.

At its core, RAG adds an extra layer of sophistication by combining the generative capabilities of models like GPT (Generative Pre-trained Transformer) with retrieval mechanisms akin to search engines or databases. It does this by first leveraging a retriever module to fetch relevant information from a corpus of documents based on the input query; this step ensures that the generation is grounded in reality and packed with factual density. Subsequently, the generator module takes this context into account to produce responses that are not only coherent and contextually apt but also rich with the nuance and detail provided by the external documents.

Applications needing a thorough comprehension and creation of knowledge-based content now have more options thanks to this creative method. RAG paves the way for the next phase of AI that can learn from and leverage the infinite quantity of human knowledge to support a multitude of use cases, from more knowledgeable chatbots and virtual assistants to improved text completion tools and advanced content production. Let’s explore how Retrieval Augmented Generation is changing the language model environment and what this means for AI developments in the future.

It’s critical to define terms and acknowledge distinctions between the two methods of language modelling and generation when contrasting RAG (Retrieval-Augmented Generation) with conventional Large Language Models (LLMs).

RAG vs Traditional LLMs

Conclusion

To sum up, RAG models add a unique perspective to the table by include the retrieval of current external information to improve generation tasks. This works especially well for producing precise and up-to-date content in real time. Conventional LLMs are very adaptable and effective for a wide range of generation tasks, but they are restricted to the information they were trained on. The particular requirements of the task at hand, such as the necessity for real-time precision against the need for broad general knowledge and creative generation, will determine whether to use RAG or traditional LLMs.