Generative AI with LLMs: Retrieval Augmented Generation: How It Enhances Generation-Based Models

Learn how Retrieval Augmented Generation (RAG) works and why it is a powerful technique for enhancing generation-based models by making external knowledge available to the model.

Question

How does Retrieval Augmented Generation (RAG) enhance generation-based models?

A. By applying reinforcement learning techniques to augment completions.
B. By increasing the training data size.
C. By making external knowledge available to the model
D. By optimizing model architecture to generate factual completions.

Answer

C. By making external knowledge available to the model

Explanation

The correct answer is C. By making external knowledge available to the model. Retrieval Augmented Generation (RAG) is an AI framework that enhances generation-based models by allowing them to access external sources of knowledge, such as the internet, documents, or databases. RAG combines pre-trained language models, such as GPT-4, with a retrieval mechanism that acts as a bridge between the language model and the knowledge source. The retrieval mechanism selects the most relevant information for a given input prompt and passes it to the language model, which then generates an output that incorporates the retrieved information. This way, RAG can improve the quality, diversity, and factual accuracy of the generated outputs, as well as provide users with insight into the model’s generative process.

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