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How Retrieval-Augmented Generation Works
RAG enhances large language models (LLMs) by retrieving the most relevant and current information from external knowledge sources. Before a user can retrieve responses from a RAG pipeline, data must be ingested into the knowledge base.
Data Extraction: Multimodal, structured, and unstructured data is extracted from various formats and converted to text so it can be filtered, chunked, and fed into the retrieval pipeline.
Data Retrieval: Extracted data is passed to an embedding model to create knowledge embeddings that go into a vector database. When a user submits a query, the system embeds the query, retrieves relevant data from the vector database, reranks the results, and sends them to the LLM to return the most accurate and context-aware responses.
Explore RAG Technology
NVIDIA NeMo Retriever
NVIDIA NeMo™ Retriever is a collection of generative AI microservices for extraction, embedding, and reranking that enable developers to build pipelines that generate business insights in real time with high accuracy and maximum data privacy.
NVIDIA NeMo Agent Toolkit
NVIDIA NeMo Agent toolkit is an open-source library for framework-agnostic profiling, evaluation, and optimization of AI agent systems. By exposing hidden bottlenecks and costs, it helps enterprises scale agentic systems efficiently while maintaining reliability.
NVIDIA cuVS
NVIDIA cuVS is an open-source library for GPU-accelerated vector search and data clustering. It enables higher throughput, lower latency, and faster index build times, and improves the efficiency of semantic search within pipelines and applications such as information retrieval or RAG.
NVIDIA NeMo Curator
NVIDIA NeMo Curator provides prebuilt pipelines for generating synthetic data to customize and evaluate embedding models to improve the performance of RAG systems.
NVIDIA NeMo Customizer
NVIDIA NeMo Customizer is a high-performance, scalable microservice that simplifies fine-tuning and alignment of generative AI models, including embedding models for domain-specific use cases, making it easier to adopt generative AI across industries.
NVIDIA NeMo Evaluator
NVIDIA NeMo Evaluator is a microservice for assessing generative AI models and RAG pipelines across academic and custom benchmarks on any platform.
NVIDIA NIM
NVIDIA NIM™ is a set of easy-to-use microservices designed to accelerate the deployment of generative AI models across any cloud or data center.
Explore NVIDIA AI Blueprints That Use RAG
NVIDIA Blueprints are reference workflows for AI use cases built with NVIDIA NIM and NeMo microservices. With these blueprints, developers can build production-ready agentic AI applications that empower employees with real-time insights, connecting them to AI query engines to enable transformational efficiency and productivity gains.
Enterprise RAG
Connect secure, scalable, reliable AI applications to your company’s internal enterprise data using industry-leading embedding and reranking models for information retrieval at scale.
AI-Q Research Agent
AI-Q is an NVIDIA AI Blueprint for building AI agents that can access, query, and act on business knowledge using tools like advanced RAG and reasoning models, they transform enterprise data into an accessible, actionable resource.
AI Assistants for Customer Service
Develop secure, context-aware virtual assistants that meet the unique needs of your business and enhance customer service operations.
AI Agent for Video Search and Summarization
Ingest massive volumes of live or archived videos and extract insights for summarization and interactive Q&A.
Biomedical AI-Q Research Agent
Improve the efficiency and accuracy of various clinical development processes, including R&D, literature review, protocol generation, clinical trial screening, and pharmacovigilance.