Retrieval Augmented Generation (RAG) is an advanced AI technique that combines large language models (LLMs) with information retrieval systems to enhance the generation of relevant content. This integration allows large language models not only to generate text based on their prior training but also to access and make use of up-to-date external data in real-time.
To understand what Retrieval Augmented Generation (RAG) is, we can think of a doctor.
When a patient goes to him with a common symptom, the doctor will use their general knowledge to diagnose him and recommend treatment. However, if the case is more complex, the doctor may need to consult specialized research or seek advice from other experts to make the most informed decision.
Similarly, large language models (LLMs) are capable of answering a wide range of questions, but to provide more specific, well-supported answers, they require a system that gathers additional information. This process is called Retrieval Augmented Generation or RAG.
Retrieval Augmented Generation (RAG) is an artificial intelligence technique that combines the power of large language models (LLMs) with traditional information retrieval systems to improve the accuracy and relevance of generated answers.
Large language models (LLMs), which are part of generative AI and are trained with huge volumes of data and billions of parameters, are used to generate original answers and perform tasks such as answering questions, translating and completing sentences. However, their knowledge is limited to the data they were trained on, which can result in reduced accuracy of answers in specific subject topics or when up-to-date information is needed.
Retrieval Augmented Generation (RAG) overcomes these limitations by connecting the generative model to external information sources, such as databases, document repositories, text sets, or proprietary knowledge.
RAG relies on two key components: a retrieval model, which searches through large databases or segmented knowledge, and a generative model, which uses the retrieved information to generate natural language responses.
This approach allows RAG to supplement large language model's (LLM) training data with specific, up-to-date information without the need for retraining, making it both efficient and cost-effective.
Retrieval Augmented Generation (RAG) is particularly useful in scenarios where accessing recent or confidential information is critical, such as in corporate settings. RAG can be connected to internal knowledge databases, confidential documents, or specific business contexts, providing tailored responses.
External sources are stored in vector databases, enabling the system to perform semantic or hybrid searches, retrieving only the most relevant information for a given query. This allows RAG to generate responses that are more accurate, relevant, and contextually aware.
One major advantage of Retrieval Augmented Generation is that it customizes the user experience without the high costs of retraining the model. Instead of processing large amounts of unnecessary data, the model directly accesses the most pertinent information for the task at hand. This saves time and resources while improving accuracy in specialized domains.
Overall, RAG is a key technique for generative AI, as it overcomes the limitations of language models by supplementing them with specific, up-to-date information from external sources. This leads to a more efficient, precise, and relevant experience in tasks like content generation and complex question answering
El proceso de Generación Aumentada de Recuperación (RAG) se estructura en varias etapas que permiten mejorar la precisión y relevancia de las respuestas generadas por un modelo de lenguaje de gran tamaño (LLM).
A Retrieval Augmented Generation (RAG) process is organized into several stages to enhance the accuracy and relevance of responses generated by large language models (LLMs).
Retrieval Augmented Generation (RAG) combines retrieving external data with generating text through large language models (LLMs). The process starts by gathering relevant information for a query, then integrating this data into the LLM’s context to ensure it understands the new information. Finally, the model generates a response that is both accurate and relevant to the specific context.
By using vector databases and advanced search techniques, RAG allows the model to efficiently access up-to-date or specialized information without requiring retraining.
Before the RAG process can begin, the data to be used for retrieval must be prepared and indexed.
A Retrieval Augmented Generation (RAG) process begins with an initial query, which could be a user question or a prompt requiring a detailed answer. This query triggers the first step: retrieving relevant information.
Once the most relevant information has been retrieved, it is introduced into the language model through a process known as augmentation.
Using the enriched information from the retrieval and augmentation phases, the LLM proceeds to analyze and generate text.
Large language models (LLMs) are a core technology in artificial intelligence, especially for applications like intelligent chatbots and other natural language processing (NLP) tools. These models can generate coherent and contextually relevant responses, but they also come with notable challenges.
Since LLMs are trained on large volumes of static data, their knowledge is essentially "frozen" at a certain point in time, meaning they can't automatically access or incorporate updated information.
Moreover, their performance can be unpredictable: they may provide inaccurate or outdated responses, generate false information when unsure, or even draw on unauthorized sources without the user's knowledge.
These issues lead to a lack of user confidence, as the LLM can act like an "overconfident employee"—responding with certainty even when the information is incorrect.
To overcome the limitations of large language models (LLMs), Retrieval-Augmented Generation (RAG) provides an effective solution. RAG enhances LLM performance by connecting them to external, authoritative, and up-to-date information sources.
ncorporating RAG into an LLM-based system creates a communication bridge between the generative model and external information sources of your choosing. This offers several key benefits:
Instead of relying solely on their training data, RAG-powered models produce answers that are more in line with user expectations and specific contexts, greatly improving confidence in generative AI applications.
In its current form, ChatGPT does not directly use Retrieval Augmented Generation (RAG). Models like GPT-4 are trained on large datasets up to a certain point in time, which means they can’t access real-time information or automatically update their knowledge.
ChatGPT’s responses are based on the information it has been trained on, without access to real-time external databases, which may limit accuracy or timeliness in some contexts.
However, OpenAI has developed versions of models that can incorporate information retrieval through external tools or integrations.
For example, versions that use web browsers or specific database integrations can retrieve external data in a similar way to RAG, allowing access to real-time or domain-specific information. But this functionality is not available in all ChatGPT models by default.