Knowledge

How does RAG work? Understanding augmented generation by recovery

Written by

Nanobaly

Published on

2024-04-30

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📘 CONTENTS

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The world of artificial intelligence is full of acronyms. Recently, you may have heard of RAG (for Retrieval Augmented Generation). RAG is a technology that merges information retrieval with text generation in AI models. If we were to explain it to you pragmatically, RAG is used to optimize the results of generative AI by prioritizing an organization's specific data. This innovative approach enhances the responses generated by AI models, by enabling the dynamic integration of prompts and relevant information from external sources (not just the language model) at the time of text generation.

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L’introduction des RAG dans le domaine de l’intelligence artificielle promet de transformer la façon dont les systèmes génératifs comprennent et manipulent le langage naturel. En s’appuyant sur une base de données variée et extensive lors de la génération de réponses, les RAG permettent une amélioration significative de la qualité et de la pertinence du contenu généré, ouvrant la voie à des applications de plus en plus sophistiquées dans divers secteurs d'activité.

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What's more, RAG's application is not limited to text generation, but also extends to the creation of creative content such as music, demonstrating the versatility of this technique.

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What is Retrieval Augmented Generation (RAG)?

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Augmented Retrieval Generation is an advanced technique in natural language processing. It integrates the capabilities of generative and extractive models of artificial intelligence. It is characterized by the combination of information retrieval tools and text generation, offering rich, contextual responses. The RAG model uses a retrieval model coupled with a generation model, such as a large language model (LLM)to extract information and generate coherent, readable text.

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This method significantly enhances the search experience by adding context from additional data sources and enriching the LLM base without requiring model retraining. Information sources may include recent Internet information not included in the LLM training process, specific and/or proprietary context, or confidential internal documents.

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RAG is particularly useful for various tasks such as question answering and content generation, as it enables the AI system to use external information sources for more precise, contextual answers. It uses search methodologies, often semantic or hybrid, to respond to user intent and deliver more relevant results.

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Finally, the RAG creates enterprise-specific knowledge databases, which can be continually updated to help generative AI provide context-sensitive and appropriate responses. This technique is a significant advance in the field of generative AI and large language models, combining internal and external resources to connect AI services to up-to-date technical resources.

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The benefits of RAG for generative artificial intelligence

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RAG models offer a multitude of benefits for generative AI, improving the accuracy and relevance of responses while reducing the cost and complexity of the AI training process. Here are some of the key benefits we've identified:

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Accuracy and contextualization: RAG models are able to provide accurate and contextual answers by synthesizing information from multiple sources. This ability to process and integrate diverse knowledge makes AI responses more relevant.
Efficiency : Unlike traditional models, which require huge data sets to learn, RAG models use pre-existing knowledge sources, making them easier and less costly to train.
Updatability and flexibility: RAG models can access updated databases or external corpora, providing current information not available in the static datasets on which LLMs are usually trained.
Bias management : By carefully selecting diverse sources, RAG models can reduce the biases present in LLMs trained on potentially biased data sets. This contributes to the generation of more accurate, fairer or objective answers.
Reduced risk of error: By reducing ambiguity in user queries and minimizing the risk of model errors, also known ashallucinations"RAG models improve the reliability of generated answers.
Applicabilité aux tâches de traitement du langage naturel : Les avantages des modèles RAG ne se limitent pas à la génération de texte mais s’étendent à diverses tâches de traitement automatique du langage naturel, ce qui améliore la performance globale des systèmes IA dans des domaines variés et parfois très spécifiques.

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💡 Ces avantages positionnent les modèles RAG comme une solution puissante et polyvalente pour surmonter les défis traditionnels de l’IA générative, tout en ouvrant de nouvelles possibilités d’application dans des secteurs variés. En outre, les solutions RAG offrent des technologies avancées pour la gestion des données non structurées, la connexion à diverses sources de données, et la création de solutions d'IA générative personnalisées, marquant une évolution significative par rapport à la recherche par mots-clés traditionnelle vers des technologies de recherche sémantique.

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RAG implementation

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Implementing RAG requires a combination of programming / software development skills, and a deep understanding of machine learning and natural language processing. This technology uses vector databases to rapidly code and retrieve new data for integration into the Large Language Model (LLM). The process involves vectorizing the data, storing it in a vector database for rapid, context-sensitive retrieval of information.

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RAG implementation steps

Selecting data sources: Choose relevant sources that will provide up-to-date, contextual information.
Data ChunkingData Chunking): Segment data into easy-to-handle fragments that can be efficiently processed and indexed.
Vectorization: Convert data into digital representations that can be easily retrieved and compared.
Creating links: Create connections between data sources and generated data to ensure seamless integration.

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Challenges and best practices

Implementing RAG can be challenging, due to the complexity of the model used, the challenges associated with data preparation and the need for careful integration with language models. Seamless integration into existing MLOps workflows is essential for successful implementation.

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💡 Did you know?

RAG can be used to help lawyers and jurists draft legal documents, such as contracts or court briefs, based on databases containing legal precedents and texts. For example, when a lawyer is working on a complex contract, the RAG system can search for similar contractual clauses used in comparable cases or similar legal situations (or in a previous contract, if it concerns the same client). It then integrates this information to help draft a contract that not only meets specific legal requirements, but is also optimized to protect the client's interests in similar circumstances observed in the past!

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Some innovative uses for RAG in various sectors

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RAG is finding innovative, not to say revolutionary, applications in many sectors. It has the potential to transform interactions and processes thanks to its ability to provide precise, contextual answers. Here are just a few of the interesting applications we have identified:

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Healthcare : In medicine, GAN improves the diagnostic process by automatically retrieving relevant medical records and generating accurate diagnoses. This improves the quality of care and the speed of medical interventions.
Customer service: In the field of customer service, RAG significantly improves interaction with customers by offering personalized and contextual responses, going beyond predefined interactions and helping to improve customer satisfaction.
E-commerce: In the e-commerce sector, RAG helps personalize the shopping experience by understanding customer behaviors and preferences, offering tailored product recommendations and targeted marketing strategies. It also facilitates the creation of marketing articles, such as blog posts and product descriptions, drawing on relevant search data to reach the target audience. This ability to generate personalized marketing articles, based on relevant data, enables companies to better communicate with their target audience, providing content that truly resonates with their needs and preferences.
Finance: In finance, specialized models such as BloombergGPTtrained on huge financial corpora, improve the accuracy of the answers provided by language models, making financial consultations more reliable and relevant.

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💡 Ces utilisations démontrent la polyvalence et l’efficacité du RAG dans l’amélioration des processus et des services à travers différents domaines. Cela promet une transformation profonde des pratiques sectorielles grâce à l'utilisation de l’intelligence artificielle avancée. La variété des sujets qui peuvent bénéficier de la technologie RAG est vaste, couvrant des domaines de niche ou grand public.

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RAG challenges and considerations

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Data integration and quality in the RAG

One of the main challenges of RAG is the effective integration of retrieved information into the text generation process. The quality and relevance of retrieved information is important to ensure the accuracy of model-generated responses. What's more, aligning this retrieved information with the rest of the generated response can be complex, which can sometimes lead to errors - the so-called "hallucinations" of AI.

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Ethical and confidentiality considerations

RAG models have to navigate the murky waters of ethical considerations and confidentiality. The use of external information sources raises questions about the management of private data and the spread of biased or false information, especially if the external sources contain such information [10]. Be careful to identify fake news! Reliance on external knowledge sources can also increase data processing costs, and complicate the integration of retrieval and generation components.

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Continuous improvement and updating of knowledge

To address the limitations of large language models, such as the accuracy of information and the relevance of answers, continuous improvement is essential. Each iteration aims to enhance the efficiency and accuracy of the RAG. What's more, the RAG knowledge base can be continually updated without incurring significant costs, enabling us to maintain a rich and continually updated contextual database.

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In conclusion

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Through this article, we explored how RAG, or Recovery Augmented Generation, is revolutionizing generative AI practices by bridging the limitations of early natural language processing models. This technology promises not only to improve the accuracy, relevance and efficiency of AI-generated responses, but also to reduce the costs and complexity associated with model training. The implications of GAN extend to a variety of sectors, illustrating its potential to profoundly transform practices in many industries, through the use of generative AI offering more accurate and contextual responses, enriched by a wide range of verified data (ideally!).

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However, as with any technological advance, the implementation of GAN presents challenges, particularly in terms of integration, the quality of the information retrieved, and ethical and confidentiality considerations. Despite these obstacles, the future of RAG in improving generative AI systems is promising. At Innovatianawe support various companies in the refinement of large-scale language models (LLMs), and we are convinced that RAG will play a significant role in the ongoing evolution of automatic natural language processing and LLMs, paving the way for even more sophisticated and efficient AI systems!

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Frequently asked questions

What is RAG in the field of artificial intelligence?

RAG, which stands for "retrieval augmented generation", is a method used to improve the performance of generative artificial intelligence systems. This technique combines the text generation capabilities of an artificial intelligence model with the extraction of relevant information from an external database. When a query is posed to the system, the RAG first searches for relevant passages in the database, then uses this information to generate a more informed and accurate response.

How does RAG work?

In a RAG system, the generation model and the search model work in an integrated way. Initially, when a question is asked, the search model scans a large database for relevant information related to the question. This information is then passed to the generation model, which integrates it to produce a coherent, detailed answer. This process not only generates more precise, more complete, more natural answers, but also enriches them with specific details that are not stored directly in the generation model (which is by nature rather static).

What are the advantages of RAG?

One of the main advantages of RAG is its ability to provide more precise and contextually rich answers than a conventional generation system. By drawing on external data, it can cover a wider range of topics and provide specific details that improve the quality and credibility of answers. What's more, RAG can be particularly useful in areas requiring specific expertise or answers based on up-to-date data.

What applications can RAG be used for?

RAG applications are diverse, ranging from personalized virtual assistance to automated content creation, customer support and recommendation systems. For example, in the medical field, a RAG system can help provide answers based on the latest research publications.

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References