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A fundamental guide to master your knowledge of retrieval augmented generation

January 31, 2024

Retrieval augmented generation (RAG) has improved the function of large language models (LLM). It empowers generative AI to create more coherent and contextually relevant content. Let’s take a deeper look into understanding RAG.


What is retrieval augmented generation?


It is an AI framework and a type of natural language processing (NLP) model that enables the retrieval of information from an external knowledge base. It integrates retrieval-based and generation-based approaches to provide a robust database for LLMs.


A retrieval augmented generation model accesses a large pre-existing pool of knowledge to improve the quality of LLM-generated responses. It ensures that the information is more accurate and up-to-date by combining factual data with contextually relevant information.


By combining vector databases and LLM, the retrieval model has set up a standard for the search and navigation of data for generative AI. It has become one of the most used techniques for LLM.


retrieval augmented generation
An example illustrating retrieval augmentation – Source: LinkedIn


Benefits of RAG

While retrieval augmented generation improves LLM responses, it offers multiple benefits to the generative AI efforts of an organization.

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Improved contextual awareness


The retrieval component allows access to a large knowledge base, enabling the model to generate contextually relevant information. Due to improved awareness of the context, the output generated is more coherent and appropriate.


Enhanced accuracy


An LLM using a retrieval model can produce accurate results with proper attribution, including citations of relevant sources. Access to a large and accurate database ensures that factually correct results are generated.


Adaptability to dynamic knowledge


The knowledge base of a retrieval model is regularly updated to ensure access to the latest information. The system integrates new information without retraining the entire program, ensuring quick adaptability. It enables the generative models to access the latest statistics and research.


Resource efficiency


Retrieval mechanisms enable the model to retrieve information from a large information base. The contextual relevance of the data enhances the accuracy of the results, making the process resource-efficient. It makes handling of large data volumes easier and makes the system cost-efficient.


Increased developer control


Developers use a retrieval augmented generation model to control the information base of a LLM. They can adapt the data to the changing needs of the user. Moreover, they can also restrict the accessibility of the knowledge base, giving them control of data authorization.


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Frameworks for retrieval augmented generation


A RAG system combines a retrieval model with a generation model. Developers use frameworks and libraries available online to implement the required retrieval system. Let’s take a look at some of the common resources used for it.


Hugging face transformers


It is a popular library of pre-trained models for different tasks. It includes retrieval models like Dense Passage Retrieval (DPR) and generation models like GPT. The transformer allows the integration of these systems to generate a unified retrieval augmented generation model.


Facebook AI similarity search (FAISS)


FAISS is used for similarity search and clustering dense vectors. It plays a crucial role in building retrieval components of a system. Its use is preferred in models where vector similarity is crucial for the system.


PyTorch and TensorFlow


These are commonly used deep learning frameworks that offer immense flexibility in building RAG models. They enable the developers to create retrieval and generation models separately. Both models can then be integrated into a larger framework to develop a RAG model.




It is a Python framework that is built on Elasticsearch. It is suitable to build end-to-end conversational AI systems. The components of the framework are used for storage of information, retrieval models, and generation models.


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Use cases of RAG


Some common use cases and real-world applications are listed below.

Content creation


It primarily deals with writing articles and blogs. It is one of the most common uses of LLM where the retrieval models are used to generate coherent and relevant content. It can lead to personalized results for users that include real-time trends and relevant contextual information.


Real-time commentary


A retriever uses APIs to connect real-time information updates with an LLM. It is used to create a virtual commentator which can be integrated further to create text-to-speech models. IBM used this mechanism during the US Open 2023 for live commentary.


Question answering system


question answering through retrieval augmented generation
Question answering through retrieval augmented generation – Source: Medium


The ability of LLMs to generate contextually relevant content enables the retrieval model to function as a question-answering machine. It can retrieve factual information from an extensive knowledge base to create a comprehensive answer.


Language translation


Translation is a tricky process. A retrieval model can detect the context of phrases and words, enabling the generation of relevant translations. Access to external databases ensures the results are accurate and fluent for the users. The extensive information on available idioms and phrases in multiple languages ensures this use case of the retrieval model.


Educational assistance


The application of a retrieval model in the educational arena is an extension of question answering systems. It uses the said system, particularly for educational queries of users. In answering questions and generating academic content, the system can create more comprehensive results with contextually relevant information.



Future of RAG


The integration of retrieval and generation models in LLM is expected to grow in the future. The current trends indicate their increasing use in technological applications. Some common areas of future development of RAG include:


  • Improved architecture – the development of retrieval and generation models will result in the innovation of neural network architectures


  • Enhanced conversational agents – improved adaptation of knowledge base into retrieval model databases will result in more sophisticated conversational agents that can adapt to domain-specific information in an improved manner


  • Integration with multimodal information – including different types of information, including images and audio, can result in contextually rich responses that encompass a diverse range of media


  • Increased focus on ethical concerns – since data privacy and ethics are becoming increasingly important in today’s digital world, the retrieval models will also focus more on mitigating biases and ethical concerns from the development systems



Hence, retrieval augmented generation is an important aspect of large language models within the arena of generative AI. It has improved the overall content processing and promises an improved architecture of LLMs in the future.

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