What is RAG?
Retrieval augmented generation (RAG) is powering up large language models to give more accurate outputs. Here’s how:
When large language models (LLMs) are asked a question, they refer to their vast training data to answer. Having a huge amount of data to refer to means these models can help with an enormous range of queries. However, they don’t always get it right. Generative AI (GenAI) has some limitations, such as its tendency to provide generic answers or even generate false information.
There are several methods for gaining more reliable and accurate information from LLMs, including prompt engineering and using multiple LLMs together. Here, we'll explore a method called RAG.
What is RAG?
Retrieval augmented generation (RAG) is an advanced AI technique that combines the power of LLMs with external data sources, such as uploaded documents, ensuring that responses are grounded in specific, up-to-date information, reducing the risk of inaccuracies.
RAG has a wide range of applications across various industries. For instance, in customer support, it can power chatbots to provide precise answers by accessing product details, manuals, and customer histories.
How does RAG work?
RAG operates through four key stages: Indexing, Retrieval, Augmentation, and Generation.
Indexing: External data, such as uploaded documents, are prepared and stored for efficient retrieval.
Retrieval: When a user submits a query, relevant information is pulled from the indexed external sources.
Augmentation: The most relevant documents or pieces of information identified during the search are used to augment the original query, giving it more context.
Generation: Finally, the augmented prompt is passed to the LLM. The LLM then generates a response based on both its pre-trained knowledge and the newly provided context.
Benefits of RAG
By pulling from the external data provided, the LLM is able to find and produce more contextually relevant and up-to-date information from a reliable source than it may have otherwise. This is especially true if the external data is proprietary or constantly updating.
For example, if you asked a generic chatbot a medical question, you may get outdated, conflicting, or even entirely ‘hallucinated’ results, due to unreliable or irrelevant sources in its training data. However if you asked the same question to an LLM using RAG with external data from a reliable source, such as the NHS (National Health Service), the results would be far more accurate.
RAG vs ‘fine-tuning’
While both RAG and fine-tuning are methods to improve AI model performance, they operate differently. RAG enhances a model's responses by adding context from external sources, making it ideal for providing up-to-date, relevant information. Fine-tuning, on the other hand, involves retraining a pre-existing model on a specific dataset to improve its performance in specialised tasks.
RAG is generally more cost-efficient and flexible, as it doesn't require retraining the entire model. It's particularly useful when dealing with frequently changing information or when access to proprietary data is needed.
Future of RAG
As AI continues to evolve, RAG is poised to play an increasingly important role. Its ability to combine the broad knowledge of LLMs with specific, up-to-date information makes it a powerful tool for creating more intelligent and responsive AI systems. As organisations seek to leverage their proprietary data alongside AI capabilities, RAG is likely to become a key technology in various sectors, from e-commerce to healthcare and beyond.
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