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Large language models (LLMs), such as GPT, have transformed the AI landscape by enabling the generation of coherent and informative texts. However, a significant challenge of these models is their static nature: once trained, they cannot access up-to-date or specific information that is not present in their original dataset. This makes them less effective in contexts that require current or detailed knowledge. To address this limitation, Retrieval-Augmented Generation (RAG) has been developed, a technique that combines the generative power of LLMs with the ability to retrieve information in real time from external sources.
What is RAG?
Retrieval-Augmented Generation (RAG) is an architecture that integrates an information retrieval system with a generative model. In practice, when a question or prompt is asked, the RAG system actively searches for relevant data from external sources, such as databases, documents, or the web, and uses this information to generate a more accurate and contextualized answer. This approach allows AI models to move beyond their static nature, incorporating updated knowledge without the need for retraining.
How does RAG work?
The functioning of the RAG can be divided into four main phases:
User Input: The user provides a question or prompt to the system.
Information Retrieval: The system uses a search module to locate relevant documents or data from external sources. This data can come from internal databases, company documents or updated online sources.
Data Integration: The retrieved information is integrated into the language model, providing an enriched context for response generation.
Response Generation: The model generates a response based on both retrieved information and pre-existing knowledge, providing more accurate and up-to-date output.
RAG Process Diagram
This diagram represents the RAG workflow: the model receives a question, retrieves information from external sources, and generates an accurate and up-to-date response.
Advantages of RAG
Adopting RAG offers numerous benefits:
Continuous Update: Allows models to access recent information without the need for retraining, avoiding the related costs and solving the problem of static content.
Greater Accuracy: Reduces the risk of “hallucinations” or inaccurate responses, as information is verified and updated in real time.
Application Flexibility: It can be implemented in various sectors, adapting to different information needs and improving the reliability of the generated responses.
Applications of RAG
RAG finds application in various fields:
Customer Support: Provides accurate and up-to-date answers by drawing on real-time company documentation.
Medicine: Supports healthcare professionals by retrieving information from recent clinical studies and updated guidelines.
Academic Research: Facilitates access to scientific publications and emerging data, improving the quality of research.
Finance: Offers analysis based on current market data, aiding in investment decisions.
Education: Supports students and teachers by providing up-to-date learning materials.
Comparison between Traditional AI Models and RAG
* Hallucinations in AI models refer to the generation of incorrect or misleading information, often caused by a lack of up-to-date data or the creation of responses that are not based on real sources.
RAG in brief
Retrieval-Augmented Generation (RAG) represents an innovative solution to overcome the static nature of AI models, combining language generation with dynamic information retrieval. This approach not only improves the accuracy and relevance of responses, but also allows models to quickly adapt to information changes, making them mlore effective and reliable tools in a wide range of applications. The future of generative AI will increasingly depend on the integration of these technologies, ensuring models capable of learning and updating in real time without the need for expensive and lengthy training.
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