Retrieval Augmented Generation (RAG) is an AI methodology that builds on the now ubiquitous LLM (Large Language Model) framework.

RAG essentially allows us to harness the power of LLMs for our own applications, using our own internal data sources (and external if we want and we are permitted to) and presenting the increasingly familiar LLM prompts to internal and external users including employees, customers, patients, citizens and students.

Smart Vision Europe has a core expertise in applied data science, machine learning (ML) and artificial intelligence (AI). As the reach of these technologies have spread further and further into more and more areas of business operations so our clients have been coming to us for input and advice. Our long experience with building predictive models has proved invaluable, especially when ensuring that your AI model deployments are sufficiently robust and that they are performing in an auditable and compliant fashion without allowing undue bias to creep into the recommendations and results that they present.

These principles of model management, refresh and governance are as important for operational RAG deployments as they have always been for any form of forward looking predictive model. This has led us into this new and interesting area. In this blog post I provide an introduction to RAG and its advantages, then discuss some examples of interesting use cases that we have come across.

This hybrid approach to the AI capabilities provides multiple potential advantages:

• Probably first and foremost, the primary reason we “do RAG” is to essentially create a Generative AI channel for our own use cases. Often this will free up resources from people who are currently dealing with queries/questions for stakeholders enabling them to do other more valuable work. For example we have seen this happen already with Insights and HR teams. 
• RAG provides an additional layer of governance and security, particularly in relation to your user community, around the governance and confidence you can have in the quality, accuracy and timeliness of information that is being presented to your stakeholders.
• RAG offers an additional layer of auditability regarding what was presented, to whom and when. If your organisation is using a RAG approach as part of your AI and automation initiatives, you are in a much stronger position of control. This is important, especially where compliance is a concern and when AI bias is a potential risk.
• Following on from improved data governance and auditability as part of a hybrid approach to AI this will also enable more advanced analysis and modelling of how your user community is engaging with and using your AI platform. This creates the opportunity to better understand and predict patterns of behaviour and to anticipate future requirements.

Does our data go to the cloud?

At a high level we have 3 choices here:

1. No. We can run an LLM, including LLaMA (from Meta) and Mistral on-premise.
2. Yes but we do not allow the cloud-based LLM to use our data for training purposes.
3. Yes and allow the cloud-based LLM to use it for training purposes.

There are trade-offs here of course. For example we might prefer B so that we can use the power of ChatGPT-4 with the proviso that our data is only logged for quality monitoring, security, and optimisation purposes.

These types of consideration deserve careful thought and we will address these in more detail in a subsequent blog post, 

Use cases for RAG

Here are some key use cases for Retrieval-Augmented Generation (RAG), which highlights how it can be used across different industries and scenarios.

Customer support and chatbots

• Automating responses to customer queries by generating answers based on both existing documentation and real-time information retrieval.
• How RAG Works: When a customer asks a question, the system retrieves relevant documents (like FAQs, manuals, past tickets) and then generates a personalized response using the LLM.  
• Benefit: Provides more accurate and contextually relevant answers compared to a typical chatbot relying solely on the language model.

Document Search and Summarisation

• Enabling users to search across a vast collection of documents and generate concise summaries.
• How RAG Works: When a user enters a query, the system retrieves documents from a knowledge base or document repository and then generates a summary of the most relevant information.   –
• Benefit: Speeds up information retrieval and understanding, making it highly useful for industries like legal, academic research and consulting.

Medical Diagnosis and Knowledge Assistance

• Assisting healthcare professionals with diagnostic suggestions or treatment plan recommendations.   
• How RAG Works: The model retrieves medical literature, clinical guidelines, or similar case studies to generate informed suggestions or explanations for diagnoses or treatments.   
• Benefit: Helps doctors and medical professionals stay updated with the latest research while providing patient-specific advice.

Enterprise Search Systems

• Helping employees find relevant documents, emails, or other data within large enterprise systems.  
• How RAG Works: RAG-based systems can retrieve internal knowledge from wikis, databases, and email archives to provide concise answers or generate reports.   
• Benefit: Enhances productivity by reducing the time spent searching for information in disparate systems.

Content generation with fact checking

• Use Case: Journalists or marketers generating articles or reports that require factual accuracy and supporting evidence.   
• How RAG Works: The model retrieves relevant, verified data from trusted sources (like government databases, reputable news sources, or internal databases) to generate content that’s accurate and substantiated.   
• Benefit: Ensures the generated content is grounded in real-world facts and data, improving credibility.

Code Assistance and Documentation Generation

• Helping developers generate code snippets, API documentation, or troubleshoot issues based on internal documentation or community forums.   
• How RAG Works: When a developer queries about a function or error, the system retrieves relevant code examples, Stack Overflow answers, or internal documentation, and generates custom code snippets or explanations.   
• Benefit: Reduces the time needed to write code or solve coding issues, improving developer efficiency.

Research and Development (R&D)

• Helping R&D teams quickly access relevant academic papers, patents, or internal research data.   
• How RAG Works: By retrieving related scientific papers, research documents, or patents, the model helps generate insights or summaries for specific R&D problems.   
• Benefit: Facilitates innovation by speeding up access to useful, relevant information.

E-commerce Product Search and Recommendations

• Improving product discovery on e-commerce platforms.   
• How RAG Works: When a customer searches for a product, the system retrieves product descriptions, reviews, or similar items and generates personalized recommendations or product summaries.
• Benefit: Enhances the shopping experience by providing more personalized and accurate search results.

Legal Document Analysis

• Helping lawyers quickly analyze case files, contracts, or legal precedents.   
• How RAG Works: The system retrieves relevant legal documents, statutes, or past case rulings, and generates summaries or answers to legal queries.   
• Benefit: Improves efficiency in preparing legal cases, reducing manual research effort.

Educational tools and tutoring systems

• Creating personalized tutoring experiences where students receive detailed answers or explanations based on textbooks, academic papers, or past course materials.  
• How RAG Works: The model retrieves relevant teaching material, and then generates a clear and concise answer or explanation tailored to the student’s level.   
• Benefit: Provides a more personalized and responsive learning experience compared to static educational content.

Business Intelligence (BI) and Report Generation

• Generating business reports or insights by combining data retrieval with natural language generation.   
• How RAG Works: The system retrieves data from business intelligence tools (e.g., sales reports, market data) and generates custom reports or insights.   
• Benefit: Reduces the manual effort needed to create reports and allows for deeper analysis through natural language generation.

Personalized Recommendations in Healthcare

• Offering personalized healthcare recommendations based on a patient’s medical history.   
• How RAG Works: By retrieving a patient’s past records and combining it with general medical knowledge, the model can generate personalized healthcare suggestions.   
• Benefit: Enhances personalized care and ensures recommendations are backed by data and expert knowledge.

Each of these examples leverages RAG’s ability to integrate a knowledge base with generative models to provide accurate, contextual, and relevant responses or content generation tailored to specific use cases.