13 Oct Approaches/ Techniques of Retrieval Augmented Generation (RAG)

The RAG has some techniques to retrieve the documents and generate a response. The system searches through a large dataset to retrieve relevant documents or pieces of information based on the input query. This information is integrated into the LLM and enables it to generate more correct and context-specific text.

The result i.e. the response generated is relevant to the query and will answer the user’s question.

The below figure shows what RAG includes:

It combines the following two approaches/ techniques:

• Retrieval-based: The model searches through a large dataset to find relevant information based on a query. Consider it as googling to get some articles.
• Generation-based: The model then uses a neural network to generate a coherent and contextually accurate response. It is like summarizing those articles into a single, well-written answer.

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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)