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Retrieval Augmented Generation (RAG) is an AI framework that optimizes the output of a Large Language Model (LLM) by referencing a credible knowledge base outside of its training sources. RAG combines the capabilities of LLMs with the strengths of traditional information retrieval systems such as databases to help AI write more accurate and relevant text.
LLMs are crucial for driving intelligent chatbots and other NLP applications. However, despite their power, they have limitations like relying on static training data and sometimes providing unpredictable or inaccurate responses. They may also give outdated or incorrect information when unsure of the answer, especially for topics requiring deep knowledge. The model’s responses are limited to the perspectives in its training data, which might lead to response bias. Although LLMs are widely used today in various domains, their effectiveness in information retrieval is often hindered by these limitations.
RAG is a powerful tool that plays a significant role in overcoming the limitations of LLMs. By guiding them to relevant information from an authoritative knowledge base, RAG ensures that LLMs can provide more accurate and reliable responses. As the usage of LLMs continues to grow, the applications of RAG are also on the rise, making it an indispensable part of modern AI solutions.
Table of contents
Architecture of RAGUse Cases of RAG in Real-world ApplicationsKey ChallengesFuture Trends
Architecture of RAG
A RAG application generally works by pulling information related to the user query from the external data source, which is then passed on to the LLM to generate the response. The LLM uses both its training data and external information to provide more accurate answers. A more detailed overview of the process is as follows:
The external data can come from various sources, such as a text document, an API, or databases. This data is converted into a numerical representation by an embedding model into a vector database so that the AI model can understand the information.
The user query is then converted into a numerical representation and is matched with the vector database to retrieve the most relevant information. This is done using mathematical vector calculations and representations.
The RAG model then augments the user prompt by adding the relevant retrieved data in context, which the LLM uses to generate better answers.
The efficiency of an RAG application can be increased through techniques like query rewriting, segmenting the original query into multiple sub-queries, and integrating external tools into RAG systems. Additionally, RAG performance is dependent on the quality of data used, the presence of metadata, and the prompt quality.
Use Cases of RAG in Real-world Applications
RAG applications are widely used today across various domains. Some of their common use cases are as follows:
RAG models improve question-answering systems by retrieving accurate information from authoritative sources. A use case of RAG applications is in information retrieval in healthcare organizations, where the application can answer medical queries based on medical literature.
RAG applications are very effective in streamlining content creation by generating relevant information. Moreover, they are also very valuable in producing concise summaries of information from multiple sources.
RAG applications also enhance conversational agents, enabling chatbots and virtual assistants to provide precise and contextually relevant responses. This makes them ideal to use as customer service chatbots and virtual assistants that can provide accurate and informative responses during interactions.
RAG models are also used in knowledge-based search systems, educational tools, and legal research assistants. They can provide tailored explanations, generate study materials, help draft documents, analyze legal cases, and formulate arguments.
Key Challenges
Although RAG applications are very powerful when it comes to information retrieval, there are a few limitations that need to be considered to leverage RAG effectively.
RAG applications rely on external data sources, and building and maintaining integrations with 3rd party data might be challenging and require technical expertise.
3rd party data sources might include personally identifiable information that can lead to privacy and compliance issues.
Latency in response is another challenge that can arise due to the size of the data source, network delays, and the increased number of queries a retrieval system must handle. For example, if a large number of users use the RAG application, then it might fail to work quickly enough.
Relying on unreliable data sources can cause the LLM to provide false or biased information and may result in incomplete coverage of a topic.
Setting up the output to include sources can be difficult, particularly when working with multiple data sources.
Future Trends
A RAG application’s utility can be further increased if it can handle not just textual information but also a wide variety of data types—tables, graphs, charts, and diagrams. This requires building a multimodal RAG pipeline capable of interpreting and generating responses from diverse forms of data. Multimodal LLMs (MLLMs), like Pix2Struct, help develop such models by enabling a semantic understanding of visual inputs, improving the system’s ability to answer questions and deliver more accurate, contextually relevant responses.
With the growth of RAG applications, there is a high demand for incorporating multimodal capabilities in order to deal with complex data. Developments with MLLMs will improve the AI’s understanding of information, further increasing its application in healthcare, education, legal research, and others. The prospect of multimodal RAG systems is likely to widen the scope of the application of AI across industries.
References:
https://aws.amazon.com/what-is/retrieval-augmented-generation/
https://cloud.google.com/use-cases/retrieval-augmented-generation
https://hyperight.com/7-practical-applications-of-rag-models-and-their-impact-on-society/
https://developer.nvidia.com/blog/an-easy-introduction-to-multimodal-retrieval-augmented-generation/
https://hyperight.com/6-ways-for-optimizing-rag-performance/
https://www.merge.dev/blog/rag-challenges
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“}]] [[{“value”:”Retrieval Augmented Generation (RAG) is an AI framework that optimizes the output of a Large Language Model (LLM) by referencing a credible knowledge base outside of its training sources. RAG combines the capabilities of LLMs with the strengths of traditional information retrieval systems such as databases to help AI write more accurate and relevant text.
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