Hello, readers; I'm thrilled to have you join me as we explore an exciting trend in the industry known as Retrieval Augmented Generation (RAG). This cutting-edge technique is transforming how we handle customer queries, offering superior accuracy compared to traditional chatbots. With RAG, our chatbots can tap into recent data, providing more relevant responses—a capability many pre-trained Language Models (LLMs) lack. An Example to start with Let's dive into a common scenario: picture yourself managing an online retail giant like Walmart or Amazon. Your customers frequently inquire about products and other stuff. However, given the ever-changing nature of your inventory, constantly re-training a chatbot becomes impractical over time. That's where RAG heads up. By leveraging your product catalog, RAG swiftly retrieves relevant information from your data store or the knowledge base. It then generates a response with this information. This approach not only reduces training costs but also ensures seamless adaptation to dynamic data in modern businesses. Large Language Models (LLM’s) Before delving into the central idea of RAG, let's first examine a more familiar and straightforward concept: Large Language Model (LLM). Natural Language Processing (NLP) specialized models, such as LLMs are excelling at generating text by leveraging the extensive data they've been exposed to during training. These models, including ChatGPT, utilize transformer architectures to grasp the context and meaning of the input data. A famous example of a chatbot using an LLM model is the ChatGPT3.5 and GPT-4 models. In terms of LLMs, we encounter two main issues. One is the scarcity of knowledge about a specific domain that was not presented during training of LLM. The second is the generation of inaccurate and unreliable responses, often referred to as “hallucinations” resulting from insufficient exposure to recent data. To tackle these problem, an alternative approach is to fine-tune the LLM on domain-specific information. However, we will now explore a more straightforward solution involving Retrieval Augmented Generation (RAG). Retrieval Augmented Generation (RAG) Retrieval Augmented Generation (RAG) is an NLP technique that combines retrieval and generative AI models. It leverages the strengths of both retrieval models, which are good at retrieving accurate information from various data sources, and generative models, which excel at producing unique and human-like responses. By combining these capabilities, RAG aims to provide accurate and human-like responses while overcoming the limitations of each model type.  In a RAG-based AI system, a retrieval model is used to find relevant information from existing data sources while the generative model takes the retrieved information, synthesizes all the data, and shapes it into a coherent and contextually appropriate response. Components of a RAG system The primary components of the RAG include: Indexing documents: In this step, data from various sources are collected, preprocessed, and broken into chunks before being indexed into a vector database. We have previously discussed vector databases in a previous blog.
Retrieval: Once the documents are indexed, during the inference time, accurate documents are retrieved from the vector database using the similarity score between the input query and document embeddings.
Generation: After retrieving the accurate data, it is passed to a Generative AI to generate a unique and human-like response. The RAG process utilises the advantages of the other two models to deliver a more accurate answer. These valuable resources can help construct a Retrieval Augmented Generation model. Vector databases like LangChain FAISS, ChromaDB, Weaviate DB, Pinecone, Qdrant DB, and Milvus; and generative models such as GPT, Llama2, HuggingFace, an open-source platform with numerous pre-trained models for NLP, computer vision, and more. How RAG is being used today? Retrieval Augmented Generation can enhance business customer support by creating advanced chatbots that deliver swift and precise responses, resulting in increased customer satisfaction. Furthermore, this technology can generate informative knowledge base articles and help documents for your business by combining generative capabilities with domain-specific data retrieval. Resources Here are some other resources you may find helpful: IBM research blog on RAG
Nvidia’s blog on RAG In conclusion, Retrieval Augmented Generation (RAG) marks a significant advancement in natural language processing by integrating retrieval and generative AI models. It effectively addresses the limitations of traditional chatbots and large language models, providing accurate and contextually relevant responses. Through its capability to index, retrieve, and generate information, RAG greatly enhances customer support and knowledge systems. This technology offers businesses a valuable tool for improving interactions, promising enhanced customer experiences in the digital realm. Its adaptability to dynamic data and specific queries positions RAG as a promising asset for driving business success and customer satisfaction through efficient dialogue systems. Wishing you an enjoyable and fruitful learning journey! Hello, readers; I'm thrilled to have you join me as we explore an exciting trend in the industry known as Retrieval Augmented Generation (RAG). This cutting-edge technique is transforming how we handle customer queries, offering superior accuracy compared to traditional chatbots. With RAG, our chatbots can tap into recent data, providing more relevant responses—a capability many pre-trained Language Models (LLMs) lack. An Example to start with Let's dive into a common scenario: picture yourself managing an online retail giant like Walmart or Amazon. Your customers frequently inquire about products and other stuff. However, given the ever-changing nature of your inventory, constantly re-training a chatbot becomes impractical over time. That's where RAG heads up. By leveraging your product catalog, RAG swiftly retrieves relevant information from your data store or the knowledge base. It then generates a response with this information. This approach not only reduces training costs but also ensures seamless adaptation to dynamic data in modern businesses. Let's dive into a common scenario: Large Language Models (LLM’s) Before delving into the central idea of RAG, let's first examine a more familiar and straightforward concept: Large Language Model (LLM). Natural Language Processing (NLP) specialized models, such as LLMs are excelling at generating text by leveraging the extensive data they've been exposed to during training. These models, including ChatGPT, utilize transformer architectures to grasp the context and meaning of the input data. A famous example of a chatbot using an LLM model is the ChatGPT3.5 and GPT-4 models. In terms of LLMs, we encounter two main issues. One is the scarcity of knowledge about a specific domain that was not presented during training of LLM. The second is the generation of inaccurate and unreliable responses, often referred to as “ hallucinations” resulting from insufficient exposure to recent data. To tackle these problem, an alternative approach is to fine-tune the LLM on domain-specific information. However, we will now explore a more straightforward solution involving Retrieval Augmented Generation (RAG). hallucinations” Retrieval Augmented Generation (RAG) Retrieval Augmented Generation (RAG) is an NLP technique that combines retrieval and generative AI models. It leverages the strengths of both retrieval models, which are good at retrieving accurate information from various data sources, and generative models, which excel at producing unique and human-like responses. By combining these capabilities, RAG aims to provide accurate and human-like responses while overcoming the limitations of each model type.  In a RAG-based AI system, a retrieval model is used to find relevant information from existing data sources while the generative model takes the retrieved information, synthesizes all the data, and shapes it into a coherent and contextually appropriate response. Components of a RAG system The primary components of the RAG include: Indexing documents: In this step, data from various sources are collected, preprocessed, and broken into chunks before being indexed into a vector database. We have previously discussed vector databases in a previous blog. Retrieval: Once the documents are indexed, during the inference time, accurate documents are retrieved from the vector database using the similarity score between the input query and document embeddings. Generation: After retrieving the accurate data, it is passed to a Generative AI to generate a unique and human-like response. The RAG process utilises the advantages of the other two models to deliver a more accurate answer. Indexing documents: In this step, data from various sources are collected, preprocessed, and broken into chunks before being indexed into a vector database. We have previously discussed vector databases in a previous blog . Indexing documents: previous blog Retrieval: Once the documents are indexed, during the inference time, accurate documents are retrieved from the vector database using the similarity score between the input query and document embeddings. Retrieval: Generation: After retrieving the accurate data, it is passed to a Generative AI to generate a unique and human-like response. The RAG process utilises the advantages of the other two models to deliver a more accurate answer. Generation: These valuable resources can help construct a Retrieval Augmented Generation model. Vector databases like LangChain FAISS, ChromaDB, Weaviate DB, Pinecone, Qdrant DB, and Milvus; and generative models such as GPT, Llama2, HuggingFace, an open-source platform with numerous pre-trained models for NLP, computer vision, and more. These valuable resources can help construct a Retrieval Augmented Generation model. Vector databases like LangChain FAISS, ChromaDB, Weaviate DB, Pinecone, Qdrant DB , and Milvus ; and generative models such as GPT, Llama2, HuggingFace, an open-source platform with numerous pre-trained models for NLP, computer vision , and more. LangChain FAISS, ChromaDB, Weaviate DB, Pinecone, Qdrant DB Milvus GPT, Llama2, HuggingFace, an open-source platform with numerous pre-trained models for NLP, computer vision How RAG is being used today? Retrieval Augmented Generation can enhance business customer support by creating advanced chatbots that deliver swift and precise responses, resulting in increased customer satisfaction. Furthermore, this technology can generate informative knowledge base articles and help documents for your business by combining generative capabilities with domain-specific data retrieval. Resources Here are some other resources you may find helpful: IBM research blog on RAG Nvidia’s blog on RAG IBM research blog on RAG IBM research blog on RAG Nvidia’s blog on RAG Nvidia’s blog on RAG In conclusion, Retrieval Augmented Generation (RAG) marks a significant advancement in natural language processing by integrating retrieval and generative AI models. It effectively addresses the limitations of traditional chatbots and large language models, providing accurate and contextually relevant responses. Through its capability to index, retrieve, and generate information, RAG greatly enhances customer support and knowledge systems. This technology offers businesses a valuable tool for improving interactions, promising enhanced customer experiences in the digital realm. Its adaptability to dynamic data and specific queries positions RAG as a promising asset for driving business success and customer satisfaction through efficient dialogue systems. Wishing you an enjoyable and fruitful learning journey!

This story contains AI-generated text. The author has used AI either for research, to generate outlines, or write the text itself. 

Large Language Models: A Beginner's Journey—Part 1

Vector Databases: Getting Started With ChromaDB and More

Revolutionising Chatbots: The Rise of Retrieval Augmented Generation (RAG)

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