Access to specific up-to-date information
Traditional language models are usually trained on static data, and can therefore become obsolete. In contrast, RAG systems retrieve information from external sources, such as databases, APIs, or updated documents in real-time.

This is essential for industries in which data changes frequently, such as the medical, legal, or banking sectors.

Reducing model size
One advantage of RAG systems is that they can be used with smaller models compared to larger, purely generative models such as GPT. This is because RAG models do not need to store the information in the model parameters, since the necessary information is retrieved from an external memory.

As a result, these systems require less computational resources for training and storage.

Greater contextual accuracy
Concerning the relevance of the information, a RAG system can retrieve valuable information directly from reliable sources, lessening possible errors presented by the ‘hallucinations’ that generative models usually address.

This information retrieval is performed through algorithms such as BM25; semantic embeddings or neural search models to ensure that the retrieved data is high-quality.

Adaptability in data management
A RAG system allows multiple heterogeneous data sources usage (structured and unstructured). For example, it can combine SQL databases, PDF documents, and websites  information.

In fact, natural language processing (NLP) can change unstructured data into useful information for the generator.

Optimization
By separating retrieval and generation, the system can easily scale in large volumes of data environments. Searches can be optimized independently of the generating model.

RAG systems often use very efficient search engines: for instance FAISS. This engine is specifically designed for searching large amounts of data.

Customization
Customizing a RAG system for a specific business is simple. The retrieval database just needs to be updated rather than retraining the entire model.

This considerably reduces costs and times to achieve the update, as the generator continues to operate using the same capabilities.

Biases mitigation
By providing context from reliable sources, RAG systems can limit the existing biases in the generative model training data.

Thus, the retrieval phase can result in filtered outcomes by including only data from verified sources.