How Does RAG Work? Now that you understand what RAG is, let’s look at the steps involved in setting up this framework: Step 1: Data collection You must first gather all the data that is needed for your application. In the case of a customer support chatbot for an electronics company,...
Getting the best performance for RAG workflows requires massive amounts of memory and compute to move and process data. TheNVIDIA GH200 Grace Hopper Superchip, with its 288GB of fast HBM3e memory and 8 petaflops of compute, is ideal — it can deliver a 150x speedup over using a CPU. On...
A lesser-known yet critical factor is theethical dimensionof RAG. Addressing biases in retrieval mechanisms ensures equitable outcomes, particularly in sensitive fields like healthcare and legal systems. This requirestransparent dataset curationandalgorithmic accountability. By connecting RAG to disciplines li...
There are dozens of them, but some of the more common ones are retrieve and re-rank, which needs a re-ranking model; multi-modal RAG, which needs a multi-modal LLM; graph RAG, which needs a graph database in addition to a vector database; and agentic RAG, which needs AI agents....
Amazon Kendra is a managed information retrieval and intelligent search service that uses natural language processing and advanced deep learning model. Unlike traditional keyword-based search, Amazon Kendra uses semantic and contextual similarity—and ranking capabilities—to decide whether a text chunk or...
Not sure what you’re looking for?View all Related Programs Best Practices for Generative Engine Optimization (GEO) Ready to get started with GEO? Here are some practical tips: 1. Understand User Intent Focus on what your audience is asking. What problems are they trying to solve? Tailor you...
What is Grounding? Grounding is the process of using large language models (LLMs) with information that is use-case specific, relevant, and not available as part of the LLM's trained knowledge. It ...
There are dozens of them, but some of the more common ones are retrieve and re-rank, which needs a re-ranking model; multi-modal RAG, which needs a multi-modal LLM; graph RAG, which needs a graph database in addition to a vector database; and agentic RAG, which needs AI agents. ...
What is Grounding? Grounding is the process of using large language models (LLMs) with information that is use-case specific, relevant, and not available as part of the LLM's trained knowledge. It ...
Post processing.After a vector database retrieves a query vector’s nearest neighbors, it may optionally re-rank the rows of the result set. Re-ranking is an expensive operation compared with the vector query, but it can give a better order for the existing vector query results. ...