Use RAG when you need language output that is constrained to some area or knowledge base, when you want some degree of control over what the system outputs, when you don’t have time or resources to train or fine-tune a model, and when you want to take advantage of changes in foundatio...
An AI technique called retrieval-augmented generation (RAG) can help with some of these issues by improving the accuracy and relevance of an LLM’s output. RAG provides a way to add targeted information without changing the underlying model. RAG models create knowledge repositories—typically based...
“It’s much easier, I believe, for a state agency to approach improving the responses through a RAG capability than a fine-tuning capability,” he says. “Because the model that you use and the skill set it takes to fine-tune a model to be responsive is much more complex than establis...
What Is RAG? Retrieval Augmented Generation (RAG) is a technique that enhances LLMs by integrating them with external data sources. By combining the generative capabilities of models like GPT-4 with precise information retrieval mechanisms, RAG enables AI systems to produce more accurate and contextu...
Get to know and directly engage with senior McKinsey experts on RAG. Lareina Yeeis a senior partner in McKinsey’s Bay Area office, whereMichael Chuiis a senior fellow andRoger Robertsis a partner;Mara Pomettiis a consultant in the London office;Patrick Wollneris a consultant in the Vienna ...
Retrieval augmented generation (RAG) is an architecture for optimizing the performance of an artificial intelligence (AI) model by connecting it with external knowledge bases.
Build a Custom RAG AI Agent Combine the power of the latest LLMs with your unique enterprise knowledge. Botpress is a flexible and endlessly extendable AI chatbot platform. It allows users to build any type of AI agent or chatbot for any use case – and it offers the most advanced RAG sy...
This is the augmented generation part of things. Elements of a RAG pipeline There are countless ways to implement a RAG pipeline, but all require the same core elements. LLMs. The large language model is what's used to process the initial input to figure out what context is required ...
Once the query is understood, RAG taps into a range of external data sources. These sources could include up-to-date databases, APIs, or extensive document repositories. The goal here is to access a breadth of information that extends beyond the language model's initial training data. This st...
What is retrieval augmented generation (RAG)? An AI model is only as good as what it’s taught. For it to thrive, it needs the proper context and reams of factual data, not generic information. An off-the-shelf LLM is not always up-to-date, nor will it have trustworthy access to ...