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 support bots, and applications requiring real-time knowledge retrieval.
From the blog
Fine-tuning your custom ChatGPT chatbot
Finetuning your custom chatbot is a crucial step in ensuring that it can answer your visitors questions correctly and with the best possible information.
Herman Schutte
Founder
Handling Unresolved Support Tickets: Escalating To Human Agents
As amazing and helpful as your ChatGPT powered custom chatbot might be, sometimes your customers or visitors still need a human touch. That's where escalating to human support comes in.
Herman Schutte
Founder