Knowledge

How does RAG work? Understanding augmented generation by recovery

Written by

Nanobaly

Published on

2024-04-30

Reading time

min

The world of artificial intelligence is full of acronyms. Recently, you may have heard of RAG, which stands for Retrieval Augmented Generation. RAG is a technology that combines information retrieval with text generation in AI models. To explain it in practical terms, RAG is used to optimize the output of generative AI by prioritizing data specific to an organization. This innovative approach enhances the quality of responses generated by AI models by dynamically incorporating prompts and relevant information from external sources (and not just the language model itself) at the moment of text generation.

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The introduction of RAGs in the field of artificial intelligence promises to transform the way generative systems understand and manipulate natural language. By relying on a varied and extensive database when generating responses, RAGs allow a significant improvement in the quality and relevance of generated content, paving the way for increasingly sophisticated applications in various business sectors.

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Furthermore, the application of RAG is not limited to text generation, but also extends to the creation of creative content such as music, which demonstrates the versatility of this technique.

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What is Augmented Retrieval Generation (RAG)?

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Retrieval-Augmented Generation is an advanced technique in natural language processing. It combines the capabilities of both generative and extractive artificial intelligence models. This approach is characterized by the integration of tools that retrieve relevant information and generate text, delivering rich and context-aware responses. The RAG model pairs a retrieval system with a generation model—such as a large language model (LLM)—to extract information and produce coherent, readable text.

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This method significantly improves the search experience by adding context from additional data sources and enriching the LLM database 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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The RAG is particularly useful for various tasks such as answering questions and generating content, as it allows the AI system to use external information sources for more accurate and contextual answers. It uses search methodologies, often semantic or hybrid, to meet user intent and provide more relevant results.

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Finally, the RAG creates business-specific knowledge databases that can be continuously updated to help generative AI provide contextual 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 costs and complexity of the AI training process. Here are some of the main benefits we identified:

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Precision and contextualization: RAG models are capable of providing accurate and contextual answers by synthesizing information from multiple sources. This ability to process and integrate diverse knowledge makes AI responses more relevant.
Effectiveness : Unlike traditional models that require huge data sets for learning, RAG models use pre-existing knowledge sources, making them easier and less expensive to train.
Update and flexibility: RAG models can access up-to-date databases or external corpora, thus providing current information that is not available in the static data sets that LLMs are typically trained on.
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 fairer, more equitable, or objective responses.
Reduced risk of error: By reducing ambiguity in user requests and by minimizing the risks of model errors, also known as ”hallucinations”, RAG models improve the reliability of the responses generated.
Applicability to natural language processing tasks: The benefits of RAG models are not limited to text generation but extend to various tasks of natural language processing, which improves the overall performance of AI systems in various and sometimes very specific areas.

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💡 These advantages position RAG models as a powerful and versatile solution to overcome the traditional challenges of generative AI, while opening up new application possibilities in various sectors. In addition, RAG solutions offer advanced technologies for managing unstructured data, connecting to various data sources, and creating custom generative AI solutions, marking a significant evolution from traditional keyword research to semantic search technologies.

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RAG implementation

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Implementing RAG requires a combination of programming/software development skills, and a thorough understanding of machine learning and natural language processing. This technology uses vector databases to quickly code and search for new data to be integrated into the Large Language Model (LLM). The process involves vectorizing data, storing it in a vector database for quick and contextual retrieval of information.

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RAG implementation steps

Selecting data sources : Choose relevant sources that will provide up-to-date and contextual information.
Data Breakdown (Data Chunking) : Segment data into easily manipulated fragments that can be processed and indexed effectively.
Vectorization : Convert data into numerical representations that can be easily retrieved and compared.
Creating links : Create connections between data sources and generated data to ensure smooth integration.

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Challenges and best practices

Setting up the 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 workflows of MLops is essential for the success of the implementation.

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💡 Did you know?

RAG can be used to help lawyers and legal professionals draft legal documents such as contracts or legal briefs, by leveraging databases of legal precedents and legislation. For example, when a lawyer is working on a complex contract, a RAG-based system can search for similar clauses used in comparable cases or previous agreements (especially for 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 scenarios observed in past cases!

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Some innovative uses of RAG in various sectors

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RAG is finding innovative, if not revolutionary, applications in many sectors. It has the potential to transform interactions and processes through its ability to provide accurate and contextual responses. Here are some interesting applications that we have identified:

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In the field of health : In medicine, RAG improves the diagnostic process by automatically retrieving relevant medical records and generating accurate diagnoses. This makes it possible to improve the quality of care and the speed of medical interventions.
Customer service : In the field of customer service, the RAG significantly improves customer interaction by offering personalized and contextual responses, which goes beyond predefined interactions and contributes to the improvement of customer satisfaction.
E-commerce : In the e-commerce sector, RAG makes it possible to personalize the shopping experience by understanding customer behaviors and preferences, thus offering tailor-made product recommendations and targeted marketing strategies. It also makes it easy to create marketing articles, such as blog posts and product descriptions, using relevant research data to reach the target audience. This ability to generate personalized marketing articles based on relevant data allows businesses to better communicate with their target audience, providing content that truly resonates with their needs and preferences.
Finance : In finance, specialized models such as BloombergGPT, trained 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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💡 These uses demonstrate the versatility and effectiveness of the RAG in improving processes and services across different areas. This promises a profound transformation of sectoral practices through the use of advanced artificial intelligence. The variety of topics that can benefit from RAG technology is vast, covering niche or mainstream areas.

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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 lies in the efficient integration of retrieved information into the text generation process. The quality and relevance of the information retrieved are important to ensure the accuracy of the responses generated by the models. In addition, aligning this recovered information with the rest of the generated response can be complex, which can sometimes lead to errors, the famous “hallucinations” of AI.

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Ethical and confidentiality considerations

RAG models need 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 propagation of biased or false information, especially if the external sources contain such information. Be careful to identify fake news! Dependence 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 major language models, such as the accuracy of information and the relevance of responses, continuous improvement is essential. Each iteration aims to increase the efficiency and accuracy of the RAG. In addition, the RAG knowledge base can be updated continuously without incurring significant costs, thus making it possible to maintain a rich and continuously updated contextual database.

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In conclusion

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Through this article, we explored how RAG, or Generation Augmented by Retrieval, 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 effectiveness of responses generated by AI, but also to reduce the costs and complexity associated with training models. The implications of the RAG extend to various sectors, illustrating its potential to profoundly transform the practices of many sectors, through the use of generative AIs offering more precise and contextual answers, enriched by a wide range of verified data (ideally!).

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However, as with any technological advance, implementing RAG presents challenges, especially 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 Innovatiana, we support various companies in perfecting large language models (LLMs), and we are confident that RAG will play a significant role in the continued evolution of natural language processing and LLMs, paving the way for even more sophisticated and effective 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 AI model with the extraction of relevant information from an external database. When a query is posed to the system, RAG first searches for relevant passages in the database and 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 retrieval model work together. Initially, when a question is asked, the retrieval model scans a large database to find relevant information related to the query. This information is then passed to the generation model, which incorporates it to produce a coherent and detailed answer. This process allows the system to generate responses that are not only more precise, complete, and natural but also enriched with specific details that are not directly stored in the generative model (which is inherently static).

What are the advantages of RAG?

One of the main advantages of RAG is its ability to provide more accurate and contextually rich answers compared to a classical generative system. By relying on external data, it can cover a wider range of topics and provide specific details that enhance the quality and credibility of the responses. Additionally, RAG is especially useful in fields requiring specific expertise or answers based on up-to-date information.

What applications can be considered for RAG?

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

Agent LLM: the innovation that redefines human-computer interaction

Argilla: the ultimate tool for creating quality datasets for your LLMs?

Argilla, with Distilabel, is revolutionizing data annotation to improve datasets and the performance of language models in AI

Hallucinations of LLMs: when datasets shape the reality of AI

LLM hallucinations pose major challenges in AI. Learn how to mitigate these risks through better data annotation!