Retrieval-augmented generation (RAG) helps businesses use generative AI by connecting LLMs to internal data.
While RAG is a powerful tool for enhancing an LLM’s capabilities, it is not without its limitations. Like LLMs, RAG is only as good as the data it can access. Here are some of its specific challenges:Data quality issues. If the knowledge that RAG is sourcing is not accurate or up ...
The integration layer is the center of the RAG architecture, coordinating the processes and passing data around the network. With the added data from the knowledge base, the RAG system creates a new prompt for the LLM component. This prompt consists of the original user query plus the enhanced...
This is where retrieval augmented generation (RAG) comes in. Broadly speaking, RAG is a method for giving AI models access to additional external information that they haven't been trained on. Crucially, it allows AI models to access new and up-to-date information without needing to be retrai...
Interaction with Retriever: The generator doesn’t work in isolation; it uses the context provided by the retriever to inform its response, ensuring that the output is not just plausible but also rich in detail and accuracy. The Workflow of a Retrieval-Augmented Generation (RAG) System Image So...
RAG Architecture: End to End Architectural Workflow This diagram pictures the components of a architecture and shows the conceptual tasks and components used in the indexing and retrieval processes. Its primary goal is to highlight the different phases data go through and the shared components used ...
Here’s the basic workflow of how a Generative AI solutions with Retrieval Augmented Generation (RAG) works: Query Input: When a user submits a query, it is processed by the retriever. Information Retrieval: The retriever searches through external data sources to find relevant information. This ...
If you’re wondering whether RAG can exist without artificial intelligence, the short answer is no. The “generation” capability of RAG relies on AI systems, which means you need a generative model to produce answers to your questions or prompts. Let’s review how this process works. ...
Agentic RAG is the use of AI agents to facilitate retrieval augmented generation (RAG). Agentic RAG systems add AI agents to the RAG pipeline to increase adaptability and accuracy.
An AI workflow can automate tasks that are repetitive, error-prone, or data-intensive, functioning as an intelligent agent capable of perceiving its environment and acting autonomously. This type of workflow is common in systems where agentic AI needs to interact dynamically with its surroundings—...