What is Retrieval-Augmented Generation?

Retrieval-Augmented Generation, or RAG, is a technique that improves the quality and accuracy of the output of a large language model (LLM) by retrieving relevant information from an external knowledge source before generating a response.

RAG allows LLMs to access the most current, reliable, and specific facts, and to give users insight into the sources of the generated text. RAG can also reduce the computational and financial costs of training and updating LLMs, as well as the risks of data leakage or misinformation. RAG is used for various natural language processing tasks, such as question answering, text summarization, and dialogue generation.

What is LLM Hallucinations?

Hallucinations are the phenomenon of generating inaccurate or misleading information that is not supported by the input or the external knowledge source. Hallucinations can occur due to various reasons, such as insufficient or outdated training data, lack of context awareness, or overfitting to the data distribution. Hallucinations can harm the credibility and reliability of the output, and potentially cause harm to the user or the application.

RAG helps fix Hallucination

RAG solves hallucinations by retrieving relevant information from an external knowledge source before generating a response. RAG allows the LLM to access the most current, reliable, and specific facts, and to give users insight into the sources of the generated text. RAG can also reduce the computational and financial costs of training and updating LLMs, as well as the risks of data leakage or misinformation. RAG is used for various natural language processing tasks, such as question answering, text summarization, and dialogue generation.

RAG Tools

There are several tools that can help implement RAG for various natural language processing tasks. Here are some of them:

• RAG on Hugging Face Transformers: This is a transformer plugin that allows you to generate RAG models by combining pretrained dense retrieval and sequence-to-sequence architectures. You can use this tool to create RAG models for question answering, text summarization, dialogue generation, and morehttps://www.marktechpost.com/2024/01/10/8-open-source-tools-for-retrieval-augmented-generation-rag-implementation/.
• Haystack: This is an end-to-end RAG framework for document search provided by Deepset. You can use this tool to build scalable and flexible search pipelines with RAG and other components, such as readers, retrievers, and rankershttps://research.aimultiple.com/retrieval-augmented-generation/.
• LangChain: This is a framework designed for eliciting reasoning from language models. It simplifies the development process for creators, offering a robust foundation. LangChain facilitates Generative Search, a cutting-edge search framework leveraging LLMs and R`AG. This helps in user interactions with search engines, with popular chat-search applications utilizing RAG for enhanced search experienceshttps://blog.langchain.dev/semi-structured-multi-modal-rag/.
• LlamaIndex: This is a Python-based framework designed for constructing LLM applications. It acts as a versatile and straightforward data framework, seamlessly connecting custom data sources to LLMs. This framework offers tools for easy data ingestion from diverse sources, including flexible options for connecting to vector databases.