13 Standout RAG Use Cases That Deliver Real Value with GenAI

Have you ever been frustrated with the limited capabilities of a chatbot or virtual assistant? Perhaps they couldn't comprehend your request or provide a satisfactory answer. What if you could improve their performance and accuracy by grounding their responses in a wealth of trusted information? That's the promise of RAG, which is short for retrieval-augmented generation. RAG combines the strengths of generative models and retrieval systems to produce accurate, context-aware responses. Built on advanced Machine Learning Frameworks, this article will cover RAG use cases and examples to illustrate how to build smarter, faster, and more reliable AI systems that deliver real-world value by grounding generative models in trusted, up-to-date information.

One effective way to achieve your goals around RAG is to leverage Inference's AI inference APIs. With our solution, you can reduce the time and cost of development, and get to the business of building accurate, reliable AI systems faster.

What is Retrieval-Augmented Generation (RAG)?

Retrieval-augmented generation, or RAG, is a method that combines retrieval of external documents with generative AI to produce more accurate and informed responses. RAG enhances large language models by grounding their output in up-to-date or domain-specific information.

Here’s how it works:

RAG first retrieves relevant data for a prompt or question, then feeds that information as context to a generative AI model like a large language model before generating a response. This approach can significantly improve the accuracy of LLM applications, especially in areas like customer support chatbots and Q&A systems that need to maintain up-to-date information or access domain-specific knowledge.

What Challenges Does Retrieval-Augmented Generation (RAG) Solve?

Retrieval-augmented generation can improve the efficacy of large language model (LLM) applications by leveraging custom data. This is done by retrieving data/documents relevant to a question or task and providing them as context for the LLM. RAG has successfully supported chatbots and Q&A systems that need to maintain up-to-date information or access domain-specific knowledge.

Problem 1

• LLM models do not know your data
• LLMs use deep learning models and train on massive datasets to understand, summarize, and generate novel content.

Most LLMs are trained on a wide range of public data, so one model can respond to many tasks or questions. Once trained, many LLMs cannot access data beyond their training data cutoff point.

This makes LLMs static and may cause them to respond incorrectly, give out-of-date answers, or hallucinate when asked questions about data they have not been trained on.

Problem 2

AI applications must leverage custom data to be effective. For LLMs to give relevant and specific responses, organizations need the model to understand their domain and provide answers from their data, rather than offering broad and generalized responses.

Customization Without Retraining

For example, organizations build customer support bots with LLMs, and those solutions must give company-specific answers to customer questions. Others are building internal Q&A bots that should answer employees' questions on internal HR data. How do companies make such solutions without retraining those models?

Why Use Retrieval-Augmented Generation (RAG)?

RAG solves several critical problems that traditional AI and knowledge management systems face, such as:

• Hallucination RAG ensures that responses are based on actual documents and data rather than fabricated or mixed-up information. This grounding in real sources allows for accurate and reliable answers.
• Knowledge freshness Implementing RAG allows users to access and use up-to-date information, eliminating the limitations of outdated training data. This means organizations can incorporate new information instantly, ensuring that AI responses reflect the most current data.
• Private or proprietary knowledge access RAG can handle company-specific information, allowing it to work seamlessly with internal documents, policies, and procedures. This enables organizations to answer questions related to their unique practices and guidelines.
• Contextual accuracy. With RAG, responses are tailored based on the documents relevant to the inquiry. This ensures that answers are specific, rather than generic, leading to more valuable and actionable information.
• Knowledge discovery RAG facilitates quick and relevant information retrieval, making it easy to locate specific details within large document sets. This enhances efficiency by allowing users to find important information rapidly, even in extensive databases.

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13 Innovative RAG Use Cases for Faster, Context-Aware AI Output

1. Retrieval-augmented generation, or RAG

RAG enhances customer support chatbots by combining retrieval-based systems with generative AI to provide accurate and contextually relevant responses. When a customer asks a question, the chatbot retrieves relevant information from sources like:

• Knowledge bases
• FAQs
• Customer records

Retrieval-Augmented Generation (RAG)

It uses a generative model to craft a personalized response based on the retrieved data. This enables chatbots to handle complex queries that require up-to-date, detailed information.

For example, Shopify’s Sidekick chatbot—designed to automatically ingest Shopify store data—leverages retrieval-augmented generation (RAG) to deliver superior AI customer service by offering precise answers related to:

• Products
• Account issues
• Troubleshooting

Sidekick enhances the e-commerce experience by pulling relevant data from:

• Store inventories
• Order histories
• FAQs

This helps provide dynamic, contextually accurate responses in real time. Similarly, Google Cloud's Contact Center AI integrates RAG to offer personalized, real-time solutions, assisting customers to resolve issues faster while reducing the need for human agents.

2. Document Summarization and Search: Easy Peasy

Retrieval-augmented generation (RAG) has emerged as an efficient document summarization and search technology. It leverages advanced information retrieval techniques to enhance the capabilities of large language models (LLMs).

Efficient Retrieval with RAG

RAG systems can provide efficient results by integrating retrieval methods such as approximate nearest neighbor (ANN) algorithms with complex ranking models. For example, Google's Vertex AI Search uses a two-stage retrieval process: It first uses approximate nearest neighbor (ANN) algorithms to gather potential results quickly, then applies deep learning models for re-ranking to ensure the most relevant documents are prioritized.

This approach enhances the accuracy of search results. It allows for extracting critical information from documents, ensuring that users receive concise and contextually relevant answers without the noise of irrelevant content.

RAG in Financial Analysis

In finance, Bloomberg has implemented RAG to streamline summarization of extensive financial documents, like earnings reports, by pulling the latest data and extracting insights. This system improves analysts' decision-making by providing real-time summaries tailored to current financial contexts.

Up-to-date information is critical in fast-moving environments. It enhances the relevance of summaries provided to users and supports their strategic decisions.

3. Medical Diagnostics and Research: Making Sense of the Data

RAG (retrieval-augmented generation) marks a notable medical diagnostics and research advancement. RAG systems utilize vast medical knowledge databases, including electronic health records, clinical guidelines, and medical literature, to support healthcare professionals in making accurate diagnoses and well-informed treatment decisions.

RAG in Medical Diagnostics

Tools like IBM Watson Health exemplify this application. Utilizing natural language processing and machine learning algorithms, IBM Watson analyzes patient data against extensive medical literature, aiding doctors in diagnosing complex cases more effectively. This platform helps oncologists determine personalized treatment options based on a patient's:

• Unique genetic profile
• Latest research findings

RAG for Cancer Treatment

One notable application is IBM Watson Health, which employs RAG techniques to analyze large datasets, including electronic health records (EHRs) and medical literature, to aid in cancer diagnosis and treatment recommendations.

Watson’s ability to retrieve relevant clinical studies and generate personalized treatment plans based on individual patient profiles illustrates how RAG can optimize decision-making in healthcare settings.

RAG's Accuracy in Oncology

According to a study published in the Journal of Clinical Oncology, IBM Watson for Oncology matched treatment recommendations with expert oncologists 96% of the time, showcasing the potential of RAG to augment human expertise in medical diagnostics. Integrating such technology:

• Enhances patient outcomes
• Reduces the cognitive load on healthcare professionals.

This allows them to focus on patient care rather than data management.

4. Personalized Learning and Tutoring Systems: Your Academic Assistant

When it comes to personalized learning, RAG combines the power of large language models (LLMs) with retrieval systems to offer students more relevant and precise guidance. A notable example is RAMO (retrieval-augmented generation for MOOCs), which uses LLMs to generate personalized course suggestions and address the "cold start" problem in course recommendations.

Personalized E-Learning with RAG

Through conversational interfaces, RAMO assists learners by understanding their preferences and career goals, offering more relevant course options, and enhancing the e-learning experience. Beyond course recommendations, RAG-powered systems are used in intelligent tutoring in higher education.

Intelligent Tutoring with LLMs

LLMs, integrated with retrieval mechanisms, help create intelligent agent tutors that deliver personalized instruction and real-time student feedback. These systems adapt to individual learning paths by retrieving relevant knowledge and combining it with generated explanations to guide learners through complex topics.

For example, universities have begun deploying RAG-driven tutoring systems to assist students in navigating course materials more effectively, fostering more profound understanding and improving academic outcomes.

5. Fraud Detection and Risk Assessment: Finding the Bad Guys

Companies implementing RAG have reported significantly improved fraud detection rates compared to traditional machine learning models. This is primarily due to RAG's ability to access and incorporate real-time, relevant data during decision-making. Conventional methods rely heavily on pre-defined rules and historical data, which can be limited in scope and may miss emerging fraud patterns.

RAG for Dynamic Fraud Detection

RAG, enables dynamic, contextual data retrieval, enhancing the system’s ability to detect anomalies by integrating up-to-date, external information like newly reported fraud schemes or regulatory changes. Financial companies like JPMorgan Chase use AI-driven fraud detection systems using retrieval-augmented generation RAG models.

Real-Time Fraud Analysis Systems

These systems continuously retrieve and analyze real-time data from various sources to monitor transactions and detect potential fraud. Similar to RAG, they combine data retrieval with advanced analytics to assess transactions in context, enhancing the accuracy and responsiveness of fraud detection.

In retail banking, these systems can cross-reference transaction data with external fraud reports and blocklists to flag suspicious activities with greater precision, reducing the number of false positives that typically overwhelm traditional rule-based approaches.

6. E-commerce Product Recommendations: Finding the Right Products

Retrieval-augmented generation (RAG) is revolutionizing e-commerce product recommendations by combining generative AI and retrieval systems to provide highly personalized shopping experiences.

RAG models first retrieve the relevant product information from external knowledge bases or a company's product catalogue and then generate recommendations based on the user's preferences, search behaviour, and historical data.

Dynamic Recommendations with RAG

Unlike traditional recommendation systems that rely solely on predefined algorithms or collaborative filtering, RAG dynamically tailors suggestions by understanding specific customer needs in real-time. This results in more relevant and accurate recommendations, boosting user engagement and increasing sales.

Amazon's RAG-Enhanced Product Suggestions

For example, Amazon has integrated AI-driven recommendation engines that utilize retrieval-augmented generation (RAG) techniques to enhance e-commerce product recommendations.

The COSMO framework leverages large language models (LLMs) alongside a knowledge graph capturing commonsense relationships from customer behavior, enabling the system to generate contextually relevant suggestions.

Zalando's Personalized Fashion Recommendations

Similarly, Zalando has been experimenting with RAG models to suggest fashion items based on users' past interactions and preferences, significantly improving the shopping experience. These real-world applications of RAG showcase its potential to transform how e-commerce platforms deliver personalized shopping experiences.

7. Enterprise Knowledge Management: Getting Answers Fast

RAG models combine generative AI with retrieval mechanisms, allowing enterprises to generate contextually accurate responses by pulling relevant information from their proprietary knowledge bases. This is particularly beneficial for large companies with extensive documentation and data sources.

RAG for Efficient Information Retrieval

Using RAG, enterprises can provide employees and customers with instant, tailored answers to queries, reducing the need for manual search and improving efficiency. Siemens utilizes retrieval-augmented generation (RAG) technology to enhance internal knowledge management. Integrating RAG into its digital assistance platform lets employees quickly retrieve information from various internal documents and databases.

Siemens' RAG-Powered Knowledge Management

Users can input queries when faced with technical questions, and the RAG model provides relevant documents and contextual summaries. This approach improves response times and fosters collaboration, ensuring all employees have access to up-to-date information, ultimately driving innovation and reducing redundancy.

Morgan Stanley's RAG for Wealth Management

Another notable application is at Morgan Stanley, which uses retrieval-augmented generation (RAG) technology in its Wealth Management division to enhance its internal knowledge management and improve the efficiency of its financial advisors. The firm has partnered with OpenAI to create a bespoke solution that enables financial advisors to access and synthesize various internal insights related to quickly:

• Companies
• Sectors
• Market trends

This system retrieves data and generates explanatory text, ensuring that advisors receive precise answers to complex queries.

8. Delivery Support Chatbot: Keeping Delivery Drivers on Track

Doordash, a food delivery company, enhances delivery support with a RAG-based chatbot. The company developed an in-house solution that combines three key components: the RAG system, the LLM guardrail, and the LLM judge. When a “Dasher,” an independent contractor who does deliveries through DoorDash, reports a problem, the system first condenses the conversation to grasp the core issue accurately.

RAG-Powered Customer Support

Using this summary, it then searches the knowledge base for the most relevant articles and past resolved cases. The retrieved information is fed into an LLM, which crafts a coherent and contextually appropriate response tailored to Dasher's query. To maintain the high quality of the system’s responses, DoorDash implemented the LLM Guardrail system, an online monitoring tool that evaluates each LLM-generated response for accuracy and compliance.

Quality Control and Monitoring

It helps prevent hallucinations and filter out responses that violate company policies. To monitor the system quality over time, DoorDash uses an LLM Judge that assesses the chatbot's performance across five LLM evaluation metrics: retrieval correctness, response accuracy, grammar and language accuracy, coherence to context, and relevance to the Dasher's request.

9. AI Professor: Your New Academic Assistant

Harvard Business School's Senior Lecturer, Jeffrey Bussgang, created a RAG-based AI faculty chatbot to help him teach his entrepreneurship course. The chatbot, ChatLTV, helps students with course preparation, like:

• Clarifying complex concepts
• Finding additional information on case studies
• Administrative matters

ChatLTV's Training and Integration

ChatLTV was trained on the course corpus, including case studies, teaching notes, books, blog posts, and historical Q&A from the course's Slack channel. The chatbot is integrated into the course's Slack channel, allowing students to interact with it in private and public modes. The LLM is provided with the query and relevant context stored in a vector database to respond to a student's question.

Response Accuracy and Testing

The most relevant content chunks are served to the LLM using OpenAI's API. To ensure ChatLTV’s responses are accurate, the course team used a mix of manual and automated testing. They used an LLM judge to compare the outputs to the ground-truth data and generate a quality score.

10. Research and Content Creation: Finding Reliable Sources Fast

Writers, journalists, and researchers can use RAG to pull accurate references from trusted sources, simplifying and speeding up the fact-checking process and information gathering. Whether drafting an article or compiling data for a study, RAG makes it easy to quickly access relevant and credible material, allowing creators to focus on producing high-quality content.

11. Educational Tools: Personalized Learning Tools

RAG can be used in educational platforms to provide students with detailed explanations and contextually relevant examples, drawing from various educational materials. Duolingo uses RAG for personalized language instruction and feedback, while Quizlet employs it to generate tailored practice questions and provide user-specific feedback.

12. Decision Support Systems: Making Decisions Easier

Managers and decision-makers often need access to up-to-date information from various sources to make informed decisions. RAGs can provide a consolidated view by retrieving data from multiple channels, summarizing it, and presenting actionable insights. This reduces the time spent on research and offers a holistic view of strategic decisions.

Example Use Case

A financial analyst could use a RAG-powered system to pull data from market trends, competitor reports, and internal financials and generate a report that assists the company’s executive team make strategic investment decisions.

13. Research and Development (R&D): Accelerating Progress

For companies in R&D-heavy sectors like pharmaceuticals, technology, or engineering, RAGs can assist in retrieving research papers, patents, technical documentation, and more. Instead of manually combing through countless papers and documents, RAGs can retrieve and summarize key findings.

RAG for Research Acceleration

This would accelerate the research process and allow researchers to focus on innovation. They can also generate insights from these sources, helping researchers stay up-to-date with the latest developments in their field. Moreover, since RAGs can integrate information from various fields, they can assist in finding novel insights that may not be apparent from a single discipline.

Example Use Case

A pharmaceutical company could use a RAG-powered tool to scan the latest medical research on a particular compound. The tool can pull out key findings and generate a report highlighting the potential benefits and risks for further investigation.

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