With the explosion of generative AI tools available for providing information, making recommendations, or creating images, LLMs have captured the public imagination. Although we cannot expect an LLM to have all the information we want or sometimes even include inaccurate information, consumer enthusiasm for using generative AI tools continues to build. This blog post will give you the ins and outs of RAG and LLM’s.
Introduction to RAG and LLM
One popular type of Language Model is called a Large Language Model (LLM). LLMs are designed to analyze and generate text by learning patterns and relationships from vast data. They can understand the context of a sentence and generate coherent responses based on that understanding. LLMs have played a crucial role in improving language-based applications and have become essential to the NLP community.
However, LLMs have their limitations. While they can generate text that appears human-like, they cannot often provide precise and accurate answers to specific questions. This is where Retrieval Augmented Generation (RAG) comes into the picture. RAG is a new approach combining the strengths of both retrieval- and generation-based systems. It aims to enhance the capabilities of LLMs by incorporating a retrieval component. Instead of relying solely on the learned patterns of text, RAG retrieves relevant information from a large knowledge base and then generates a response based on that retrieved knowledge.
Understanding Language Models (LLM)
Language Models (LMs) are computer programs that have undergone remarkable advancements in recent years. These models are trained on vast amounts of text data, which enables them to understand and generate human-like text. LLMs can analyze and process text in a way that allows them to generate coherent and contextually relevant responses. They learn patterns and relationships from the data they are trained on, enabling them to understand the meaning and context of sentences.
The training data for LLMs can consist of billions of sentences, resulting in models with a vast understanding of language. For instance, OpenAI's GPT-3, one of the most advanced LLMs, was trained on approximately 570GB of text data, equivalent to over 45 terabytes of uncompressed text. LLMs have an impressive number of parameters, the variables the model learns during training. The number of parameters determines the complexity and capabilities of the model. For example, GPT-3 has a staggering 175 billion parameters, making it one of the largest language models ever created.
How RAG Differs from Traditional LLMs
Architecture
Traditional LLMs typically follow an encoder-decoder architecture, where the encoder processes the input text and the decoder generates the output. In contrast, RAG incorporates a retrieval component alongside the encoder-decoder architecture. This retrieval component retrieves relevant information from a knowledge base, which is then used to augment the generation process.
Knowledge Base Integration
RAG leverages a large knowledge base, such as Wikipedia or other specialized databases, to retrieve information relevant to the input query or context. This integration allows RAG to access a vast amount of factual information and use it to generate more accurate and informed responses. Traditional LLMs, on the other hand, do not have direct access to such external knowledge sources.
Contextual Understanding
RAG's retrieval component gives it a better contextual understanding of the input query or prompt. By retrieving and incorporating relevant information from the knowledge base, RAG can generate more contextually accurate responses and aligned with the user's intent. Traditional LLMs primarily rely on learned patterns from training data and may struggle with providing precise context-based answers.
Answer Precision
Traditional LLMs often generate creative and diverse responses but may lack precision when it comes to specific queries or questions. RAG, with its retrieval component, has the potential to provide more precise and factually grounded answers. By retrieving information from the knowledge base, RAG can offer responses supported by the available facts, making it suitable for applications that require accurate and reliable information.
Training Paradigm
Traditional LLMs are typically trained using large-scale unsupervised learning, where the model learns patterns and relationships in the data without explicit supervision. RAG, on the other hand, requires a different training paradigm that involves both pre-training and fine-tuning. The retrieval component is typically pre-trained on a retrieval task, while the encoder-decoder component is fine-tuned on a generation task using human-generated responses.
Advantages of RAG in Language Understanding
RAG (Retrieval-Augmented Generation) offers several advantages in language understanding compared to traditional language models. Here are some key advantages of RAG:
Contextual Relevance
RAG excels in providing contextually relevant responses. RAG can retrieve information specifically related to the input query or context by integrating a retrieval component that accesses a knowledge base. This enables RAG to generate responses that are more closely aligned with the user's intent and the conversation context.
Accurate and Informed Responses
RAG leverages the information retrieved from the knowledge base to enhance the accuracy and informativeness of its generated responses. By incorporating factual information from reliable sources, RAG can provide more precise and reliable answers to queries. This is particularly valuable in applications where accuracy and correctness are essential, such as question-answering systems or information retrieval tasks.
Knowledge Base Integration
RAG's ability to integrate a knowledge base seamlessly enhances its understanding of the world. The retrieval component allows RAG to access vast information, including facts, definitions, historical events, and other relevant details. This integration enables RAG to generate responses not limited to the knowledge it learned during training but can incorporate up-to-date information from external sources.
Improved Query-Answering
RAG's retrieval component enables it to excel in query-answering tasks. By retrieving specific information related to the query, RAG can provide precise answers to questions rather than generating generic or ambiguous responses. This makes RAG well-suited for applications involving complex queries or information retrieval tasks where accurate and targeted answers are crucial.
Context Preservation
RAG's architecture allows it to preserve and understand the context of a conversation better. By combining retrieval and generation, RAG can maintain a coherent and consistent conversation flow by referring to previously retrieved information. This contextual understanding helps RAG generate more contextually appropriate and coherent responses to the ongoing discussion.
Flexibility and Adaptability
RAG's retrieval component allows the model to adapt to domains or knowledge bases. By training the retrieval component on relevant sources, RAG can specialize in specific domains or tailor its knowledge base to suit particular applications. This adaptability makes RAG a versatile model that can be customized for various use cases.
Final Thoughts
RAG offers an effective way to customize AI models, helping to ensure outputs are up to date with organizational knowledge, best practices, and the latest information on the internet. Context is everything in getting the most out of an AI tool. To improve the relevance and quality of a generative AI output, you need to improve the relevance and quality of the input.
At Vectorize.io, we bridge the gap between AI promise and production reality. We’ve helped leading brands unlock the power of Retrieval Augmented Generation (RAG) to revolutionize their search platforms. Now, we’re bringing this expertise to information portals, manufacturers, and retailers, helping them adapt and thrive in the age of AI-powered search.
