How Retrieval-Augmented Generation (RAG) is increasing trust in AI

What is RAG and how does it work?

Retrieval-Augmented Generation (RAG) is a technique used in natural language processing (NLP) that combines retrieval-based and generation-based approaches to improve the performance of language models. It aims to enhance the quality of text generation by supplementing the model’s responses with relevant information retrieved from a large external database or corpus of documents.

How does it work?

Retrieval: The model first retrieves relevant pieces of information from an external knowledge base, document collection, or search engine. These documents or text snippets can be retrieved using search queries based on the user's input.

Generation: After retrieving the relevant information, the language model generates a response that incorporates the retrieved content. Instead of relying solely on its pre-trained knowledge, the model uses the additional data to generate more accurate, up-to-date, or contextually relevant responses.

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Putting Theory to Practice: Samsung SDS Case Study

A review of the technical support requests received by the Samsung Cloud Platform (SCP) showed that 68% of the cases were resolved by the users who reported them by following troubleshooting guides. This raised the question of whether Retrieval Augmented Generation (RAG) could be leveraged to streamline Kubernetes troubleshooting. AI-based cluster diagnostic tool, SKE-GPT, was the answer to that question.

What did SKE-GPT do?

SKE-GPT has two primary components: a diagnostic area and an analysis area. The diagnostic area examines the status of a given data cluster based on a set of predefined rules to identify issues. Problematic data clusters are further reviewed in the analysis area where solutions are generated by AI with the aid of RAG to ensure viability of the provided solutions.

Did it work?

Ultimately, SKE-GPT was able to deliver accurate solutions quickly, performing comprehensive diagnoses and powerful scanning with a very simple command structure and interface. This significantly improved Samsung SDS’ troubleshooting capabilities.

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What did we learn from Samsung SDS’ use of RAG?

Given Samsung SDS’ effective use of Retrieval-Augmented Generation (RAG), we can draw several key takeaways:

Enhanced Accuracy

RAG improves the accuracy of AI systems by enabling real-time access to relevant external information. This ensures that generated responses are grounded in up-to-date and specific data, avoiding the limitations of static training sets.

Contextual Relevance

By integrating external knowledge bases (e.g., databases, troubleshooting guides, etc.), RAG allows the model to generate more contextually appropriate and precise responses, particularly in complex scenarios.

Scalability

RAG enables scalable solutions for dynamic and diverse queries. AI systems can handle a wide array of requests without requiring exhaustive retraining, as the model can pull in relevant information on the fly.

Flexibility in Application

RAG can be tailored to various industries, from healthcare to e-commerce, showing its versatility in enhancing both simple and complex interactions with AI.

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What does this mean for your business?

Faster Decision-Making

RAG enables quick access to the latest market trends, research papers, or internal company data, empowering decision-makers with the most relevant insights to inform their strategies. This accelerates business operations and allows companies to remain competitive in rapidly changing markets.

Cost Reduction

By automating content generation and information retrieval, RAG can reduce the need for manual data entry, content curation, and repetitive tasks. This results in lower operational costs and allows employees to focus on more strategic roles.

Scalability in Knowledge Management

In industries like healthcare or finance, RAG can be used to instantly retrieve and synthesise complex medical literature, case studies, or legal documents. This enhances knowledge management systems by providing employees with precise, timely, and actionable insights, fostering efficiency in research, compliance, and patient care.

Innovations in Product Development

In sectors like technology or manufacturing, RAG can be used to gather insights from vast datasets, patents, or user feedback, accelerating the product development cycle by generating relevant ideas and solutions based on real-world data.

Ultimately, RAG's integration into business operations can lead to more intelligent systems, greater operational efficiency, and better customer experiences, positioning companies to thrive in an increasingly data-driven world.

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How can InLogic help you make the most of RAG?

Optimising Customer Support Solutions

We can help deploy RAG-powered virtual assistants or chatbots that access relevant data repositories to provide more accurate and context-aware customer support, reducing response time, increasing customer satisfaction and decreasing reliance on human agents.

Streamlining Knowledge Management

We can integrate RAG to give your employees more effective access and comprehension of your internal knowledge management system, enhancing productivity and decision-making by providing employees with timely, relevant information without the need for extensive searches.

Real-Time Competitive Intelligence

We can build systems that can provide real-time insights into market trends, competitors, and customer sentiment by pulling data from news sources, social media, and financial reports, allowing you to stay ahead of industry trends, adjust your strategies, and make informed decisions faster than your competitors.