RAG stands for retrieval-augmented generation. This acronym underscores its core principle: augmenting a base AI system or AI model by retrieving live or frequently updated data to provide more contextually informed answers.
Retrieval-augmented generation (RAG) has emerged as an effective technique in generative AI that integrates externally available data—often proprietary or domain-specific—into workflows that use large language models (LLMs). RAG retrieves relevant context and adds that as additional prompt context just before making a request, which boosts the efficiency and accuracy of AI responses beyond what would have been possible with the standalone model that could only leverage its training data set.
RAG is used to address a fundamental challenge in AI: how to keep static models current with the latest and most relevant data, even when the underlying LLM has been trained on outdated information. Common RAG applications include:
Most generative AI models learn information during a fixed training cycle. When that training ends, the model retains knowledge only up to a certain point in time or within certain data constraints. RAG extends that knowledge by pulling in fresh, relevant data from external sources at inference time—the moment a user query arrives.
For RAG to function reliably, organizations often maintain an updated corpus—comprising structured and unstructured data—readily accessible through vector databases or knowledge graphs. Properly managing this corpus involves data ingestion, cleansing, embedding, and indexing, ensuring the retrieval engine can quickly isolate contextually appropriate pieces of information.
Advancements in AI, such as expanding context windows, may appear to reduce RAG’s importance for consumers by letting models consider huge amounts of text natively. However, for enterprise-level organizations with vast amounts of data distributed across multicloud environments, they still face rapidly changing and widely distributed data sources. RAG meets this challenge by selectively drawing on the most pertinent, authorized information—without overloading a model’s context window or risking data sprawl. As AI becomes more deeply integrated into enterprise workflows, RAG is poised to remain a key strategy for delivering timely, contextually rich, and high-accuracy outputs.
F5 plays a pivotal role in enabling secure connectivity for retrieval-augmented generation (RAG) by seamlessly connecting distributed, disparate data sources across multicloud environments to AI models. As enterprises adopt advanced AI architectures, F5 ensures high-performance, secure access to corporate data using F5 Distributed Cloud Services. Distributed Cloud Services provide a unified approach to networking and security, supporting policy-based controls, an integrated web application firewall (WAF), and encryption in transit. By enabling secure, real-time, and selective data retrieval from diverse storage locations, F5 helps enterprises overcome challenges around scalability, latency, and compliance, ensuring AI models operate efficiently while safeguarding sensitive corporate information.
Learn more how F5 enables enterprise AI deployments here.