What is Retrieval Augemented Generation (RAG)?
By Team Acumentica
What is Retrieval-Augmented Generation (RAG)?
Retrieval-Augmented Generation (RAG) is an approach that blends the principles of retrieval-based methods with generative deep learning models to enhance the capabilities of language models. This technique is particularly effective for tasks that benefit from external knowledge or context beyond what’s contained in the model’s pre-trained parameters.
Here’s a breakdown of how RAG works:
1. Retrieval: The system first retrieves relevant documents or pieces of information from a large external dataset or database. This retrieval is typically powered by a search algorithm that finds content related to the input query or context.
2. Augmentation: The retrieved documents are then used to augment the input to a generative model. This means that the model doesn’t only receive the original query or prompt but also gets additional context or information from the retrieved documents.
3. Generation: With the augmented input, the generative model then produces a response or output. This output is informed both by the model’s internal knowledge (from its training data) and the external data retrieved in the first step.
The primary advantages of RAG include:
Enhanced Accuracy and Relevance: By incorporating external information, RAG models can provide more accurate and contextually relevant responses than standard models, especially for complex queries that require specific knowledge or expertise.
Scalability: RAG allows models to effectively “scale” their knowledge by accessing vast amounts of external data, rather than being limited to what was available during training.
Versatility: This approach is useful across a variety of applications, from answering detailed questions in natural language processing to improving recommendations in content filtering systems.
RAG models are particularly useful in scenarios where a model needs to combine deep understanding of language (like idiomatic expressions or complex instructions) with factual correctness and up-to-date information, which are critical in fields like medical advice, technical support, and more specialized queries in academic or professional settings.
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