13 Oct How RAG is better than standalone generative models

Let us see how RAG is better than standalone generative models like ChatGPT. We will also see what generative models include and the differences.

What is a standalone generative model

Standalone generative models generate responses based on their training data, which might be outdated. For example, ChatGPT.

How RAG is better than a generative model

RAG enhances the retrieval by incorporating real-time, relevant information retrieved from external sources. RAG is better than Standalone Generative Models. It improves the adaptability of generative models. Let us see the differences below to understand which one is better.

RAG vs Generative models

Let us see how RAG and generative models differ:

Standalone Generative Model

• Generating Excellence: Excels at generating fluent and coherent text based on pre-trained data.
• Versatility: Great for generating text across various topics within the scope of its training data.
• Speed: Quick response generation without additional retrieval steps.
• The data is not new: This relies on the information it was trained on and does not fetch new data in real-time.

RAG (Retrieval-Augmented Generation):

• Enhanced Contextual Accuracy: Combines retrieval of relevant, up-to-date documents with generation for more accurate responses.
• Adaptability: Suitable for applications where current and extensive knowledge is crucial, such as detailed question answering.
• Complexity: More computationally intensive due to the retrieval step but offers improved accuracy and relevance.
• Detailed Answers: Integrates real-time information to provide detailed responses.

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Read More:

• What is Machine Learning
• What is a Machine Learning Model
• Types of Machine Learning
• Supervised vs Unsupervised vs Reinforcement Machine Learning
• What is Deep Learning
• Feedforward Neural Networks (FNN)
• Convolutional Neural Network (CNN)
• Recurrent Neural Networks (RNN)
• Long short-term memory (LSTM)
• Generative Adversarial Networks (GANs)