Retrieval-Augmented Generation (RAG) is the process of optimizing the output of a large language model, so it references an authoritative knowledge base outside of its training data sources before generating a response.

Retrieval-Augmented Generation (RAG) is a technique that grants generative artificial intelligence models information retrieval capabilities. It modifies interactions with a large language model (LLM) so that the model responds to user queries with reference to a specified set of documents, using this information to augment information drawn ...

2024年6月11日 · Retrieval-augmented generation (RAG) is an innovative approach in the field of natural language processing (NLP) that combines the strengths of retrieval-based and generation-based models to enhance the quality of generated text.

2023年8月22日 · Retrieval-augmented generation (RAG) is an AI framework for improving the quality of LLM-generated responses by grounding the model on external sources of knowledge to supplement the LLM’s internal representation of information.

2024年12月18日 · Retrieval Augmented Generation (RAG) is an architecture that augments the capabilities of a Large Language Model (LLM) like ChatGPT by adding an information retrieval system that provides grounding data.

2024年4月22日 · RAG represents a blend of traditional language models with an innovative twist: it integrates information retrieval directly into the generation process. Think of it as having an AI that can look...

2023年12月18日 · Luckily, all that enterprises must do to extract value out of using LLMs is to augment the LLM with their own data. This can be done with retrieval augmented generation (RAG), which is showcased in the NVIDIA Generative AI Examples GitHub repo for developers.

2023年12月13日 · In this article, we discuss various technical considerations when implementing RAG, exploring the concepts of chunking, query augmentation, hierarchies, multi-hop reasoning, and knowledge graphs....

This comprehensive guide will demystify RAG, its challenges and solutions, and explore its origins, components, and working principles. From LLM challenges to its solution: The role of RAG Overview of LLM challenges. Unlike systems that utilize retrieving data in real time, LLMs put together responses only based on training data.

2023年12月18日 · RAG synergistically merges LLMs' intrinsic knowledge with the vast, dynamic repositories of external databases. This comprehensive review paper offers a detailed examination of the progression of RAG paradigms, encompassing the Naive RAG, the Advanced RAG, and the Modular RAG.

Retrieval augmented generation, or RAG, is an architectural approach that can improve the efficacy of large language model (LLM) applications by leveraging custom data. This is done by retrieving data/documents relevant to a question or task …

2023年12月5日 · RAG is a framework for improving model performance by augmenting prompts with relevant data outside the foundational model, grounding LLM responses on real, trustworthy information.

2024年11月12日 · RAG is a method that combines the strengths of traditional information retrieval systems with the generative capabilities of LLMs. It works by: Retrieval: When a user query is received, the system searches a large, up-to-date database or corpus for relevant documents.

2023年12月6日 · What is RAG? Retrieval Augmented Generation involves enhancing the output of Large Language Models (LLMs) by providing them with additional information from an external knowledge source. Explore LLM context augmentation techniques like RAG and fine-tuning in detail with out podcast now!

2023年9月13日 · Retrieval augmented generation or RAG for short is the architecture popularized by Meta in 2020 that aims to improve the performance of LLMs by passing relevant information to the model along with the question/task details. Why RAG?

Retrieval Augmented Generation (RAG) is a pattern that works with pretrained Large Language Models (LLM) and your own data to generate responses. In Azure Machine Learning, you can now implement RAG in a prompt flow.

This tutorial will give you a simple introduction to how to get started with an LLM to make a simple RAG app. RAG (Retrieval Augmented Generation) allows us to give foundational models local...

Part 1 (this guide) introduces RAG and walks through a minimal implementation. Part 2 extends the implementation to accommodate conversation-style interactions and multi-step retrieval processes. This tutorial will show how to build a simple Q&A application over a text data source.

6 天之前 · Building on the foundational concepts of RAG systems, this article outlines robust evaluation strategies for RAG pipelines and their individual components, including the LLM, the retriever, and ...

RAG is a groundbreaking framework designed to enhance the capabilities of LLMs. Instead of solely relying on the vast but static knowledge embedded during their training, RAG empowers these models to actively retrieve and reference information from external knowledge bases.

2023年10月18日 · Retrieval augmented generation (RAG) is a strategy that helps address both of these issues, pairing information retrieval with a set of carefully designed system prompts to anchor LLMs on precise, up-to-date, and pertinent information retrieved from an external knowledge store.

4 天之前 · When deploying a customized Large Language Model (LLM), your first decision is whether to use fine-tuning or embeddings-based Retrieval-Augmented Generation (RAG). Many online introductions explain the difference in a highly abstract manner. This post concentrates on the practical implementation differences between fine-tuning and RAG, which are crucial for IT …

6 天之前 · 4. Function-Calling-based LLM-RAG: defining function tools in the Jupyter notebook for the LLM to execute automatically in order to obtain contextual information for generation. In the rest of this blog, we provide brief introductions and example outputs of the four use cases. Use Case 1: Standalone LLM

2 天之前 · The combination of Retrieval-Augmented Generation (RAG) and powerful language models enables the development of sophisticated applications that leverage large datasets to answer questions effectively. In this blog, we will explore the steps to build an LLM RAG application using LangChain.

2024年12月17日 · RAG addresses LLM limitations by introducing retrieval mechanisms. It uses a retriever to fetch the most relevant information from a database (often stored as embeddings in a vector...

It is super efficient, but costlier as you are still sending a lot of context every time to the LLM, even for simpler queries, whereas RAG sends just the necessary bits. You can get issues with sending too much information and not finding the relevant bits. An important challenge to consider with CAG is the ‘lost-in-the-middle’ problem ...

Join Akash Shankaran (Intel), Ron Abellera (Microsoft), and Juan Pablo Norena (Canonical) for a tutorial on how RAG can enhance your LLMs. The session will explore how to optimize LLMs with RAG using Charmed OpenSearch, which can serve multiple services like data ingestion, model ingestion, vector database, retrieval and ranking, and LLM connector.

2 天之前 · Abstract page for arXiv paper 2501.18320: Leveraging LLM Agents for Automated Optimization Modeling for SASP Problems: A Graph-RAG based Approach. Automated optimization modeling (AOM) has evoked considerable interest with the rapid evolution of large language models (LLMs). Existing approaches predominantly rely on prompt engineering,...