What is Retrieval Augmented Generation (RAG)?
A framework that combines retrieval and generation to produce responses grounded in external knowledge, leveraging techniques like dense retrieval and knowledge retrieval.
More about Retrieval Augmented Generation (RAG):
Retrieval Augmented Generation (RAG) is an AI framework that combines a retriever model with a generator model to produce responses grounded in external knowledge. The retriever fetches relevant documents or pieces of information using methods like dense retrieval or semantic search, while the generator uses that information to create contextually accurate and fact-based outputs.
RAG frameworks are particularly effective for applications such as open-domain question answering, knowledge-grounded dialogue systems, and other knowledge-intensive tasks, ensuring outputs are accurate and well-grounded.
Frequently Asked Questions
How does RAG improve response accuracy in AI models?
It retrieves relevant knowledge from external sources, such as vector databases, ensuring generated responses are fact-based and accurate.
What are common applications of RAG?
RAG is commonly used in question answering systems, customer service bots, and applications requiring real-time knowledge retrieval.
From the blog
How AI Assistants Can Help Service Businesses Book More Jobs
Need more time and leads as a service business owner? An AI chatbot for your service business may be the solution. See how AI can help today.
Herman Schutte
Founder
Custom model training and fine-tuning for GPT-3.5 Turbo
Today OpenAI announced that businesses and developers can now fine-tune GPT-3.5 Turbo using their own data. Find out how you can create a custom tuned model trained on your own data.
Herman Schutte
Founder