What is Retrieval Augmented Generation (RAG)?

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.

What is Retrieval Augmented Generation (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

How does Retrieval Augmented Generation (RAG) work?

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.

Retrieval Augmented Generation (RAG) Architecture

Retrieval Augmented Generation (RAG) Process in Detail

1. Indexing and Data Preparation

Before the RAG process can begin, the data to be used for retrieval must be prepared and indexed.

• Initial Vectorization: The data—whether unstructured, semi-structured, or structured—is converted into numerical representations (embeddings) that the LLM can interpret. This process allows the model to efficiently retrieve relevant information when needed.
• Vector Database Storage: These embeddings are stored in a specialized vector database, optimized for fast document search and retrieval. This database is crucial for quickly accessing relevant data during the retrieval process.

2. Retrieval

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.

• Search for information: A retrieval model scans through knowledge bases, databases or external sources, depending on the context and the nature of the query. These sources can be either public documents on the Internet or internal databases of an organization.
• Data vectorization: The retrieved information is transformed into vectors within a high-dimensional space, which facilitates its classification and analysis. These vectors are stored in a vector database that allows semantic searches and optimizes the efficiency of the retrieval process.
• Relevance ranking: The retrieval model ranks the information obtained according to its relevance for the query. The most relevant documents or fragments are selected to continue to the next step.

3. Query Augmentation

Once the most relevant information has been retrieved, it is introduced into the language model through a process known as augmentation.

• Incorporation into the LLM: The retrieved fragments are integrated into the large language model, enriching the original context of the query. In this step, the original LLM input is updated to reflect the new knowledge obtained from external sources. This provides a deeper and more accurate context, allowing the model to generate better informed answers.
• Additional adjustments: In more advanced versions of RAG, additional modules can be incorporated to extend the retrieval and generation capabilities. These include the memory of the model to learn from previous queries and the use of techniques such as self-improvement or domain extension, which optimize the quality of answers in diverse contexts.

4. Generation

Using the enriched information from the retrieval and augmentation phases, the LLM proceeds to analyze and generate text.

• Response generation: The model uses the retrieved data and the provided context to generate a coherent and accurate response. This response takes into account both the user's original query and the new information added through the retrieval process.
• Post-processing: In some cases, the generated responses go through a post-processing process to ensure that they are grammatically correct, consistent and conform to the expected format. This may include reviewing the structure of the text and removing redundancies.

What is the difference between RAG and LLM?

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.

The Main Problems with LLMs:

• False Information or AI Hallucinations: LLMs can provide incorrect information if they lack accurate data.
• Outdated Data: Since model knowledge isn't updated in real time, answers may be outdated.
• Terminology Confusion: LLMs might use terms trained in different contexts, which can lead to inaccurate responses.
• Use of Unauthorized Sources: LLMs may generate responses without verifying the sources, undermining trust in the results.

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.

RAG and LLM: Retrieval-Augmented Generation as a Solution to LLM Challenges

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.

• Relevant Information Retrieval: RAG enables LLMs to access external or internal databases, such as enterprise repositories, allowing them to retrieve additional and current information in real time.
• Cross-Referencing with Authoritative Sources: By using this method, LLMs can cross-reference their static knowledge with fresh information from trusted sources, improving the quality of their responses.

Advantages of RAG in Answer Generation

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:

• Organizational Control: Organizations gain more control over LLM outputs by determining which sources the model can access for information retrieval.
• More Accurate and Up-to-Date Responses: The ability to cross-reference with authoritative sources ensures that responses are more accurate, trustworthy, and relevant to users' needs.
• Reduced Hallucinations: By integrating real-time and reliable data, RAG reduces the likelihood of LLMs generating false or irrelevant information.

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.

Does ChatGPT Use Retrieval Augmented Generation (RAG)?

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.