Are you looking to better understand advanced AI techniques like ‘RAG’ and their practical applications? This blog will explore the concept of Retrieval-Augmented Generation (RAG) and its relationship with AI, shedding light on how it enhances your AI projects’ accuracy, relevance, and adaptability.
Retrieval-Augmented Generation (RAG) is an innovative approach that has redefined AI's potential by cross-referencing AI assistant outputs with pre-approved and vetted sources like knowledge bases.
By integrating AI with external databases, RAG enables real-time data access, making AI not only more reliable but also more accessible to businesses of all sizes. RAG has rapidly gained traction across sectors, enabling more precise and context-aware applications.
By combining advanced information retrieval with natural language generation, RAG can significantly improve the accuracy, reliability, and contextual understanding of AI outputs, helping to overcome critical limitations of large language models (LLMs). As a result, AI systems can be more effective, informed, and adaptable than ever before.
In this blog, we will delve into the definition of RAG, its workings, significance, and its applications in business. Additionally, we will examine the limitations of RAG and how agentic AI seeks to address these challenges.
What does RAG in AI mean?
First coined by Patrick Lewis in the 2020 paper Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, retrieval augmented generation (RAG) refers to a two-part process that enhances artificial intelligence (AI) capabilities. It does this by connecting large language models (LLMs) with external databases to help create more accurate, reliable AI outputs.
Put simply, RAG is the process that enables technologies like Alexa or Siri to accurately answer questions like “What's the weather like today?” with accurate forecasts for your specific ZIP code. RAG helps Alexa or Siri answer by retrieving specific information from external databases –such as up-to-date weather data– and combining it with the LLM’s capabilities.
Just think of how many ways businesses can use RAG in AI. For example, in customer service, when a customer inquires about a specific company policy, such as your return or refund policy, RAG can connect your LLM with an external database to retrieve accurate information and generate a precise response.
Ultimately, RAG enhances the accuracy and reliability of LLM responses by combining AI capabilities with information from external sources. This enables businesses to create higher-quality, contextually appropriate responses that better address queries from both teams and customers alike.
How does RAG work?
RAG is a two-phase technique in natural language processing (NLP) that enhances the capabilities of LLMs by connecting them with external databases and knowledge sources to improve the accuracy and reliability of AI outputs. Here's how it works:
- Phase 1 Retrieval: An LLM processes the user's input to understand the context and determine the needed information. Then, it formulates a specific search query to retrieve relevant information from the connected external databases, only pulling verified, up-to-date information.
- Phase 2 Generative: The retrieved information is then fed back into the LLM and combined with the LLM’s internal knowledge to generate an informative, accurate response. Importantly, this response includes citations to external sources so users can verify the information.
Together, LLMs and RAG can deliver higher-quality responses. A controlled study found that RAG increases LLM accuracy by nearly 40% on average. Without RAG, LLMs can only generate responses based on their internal training data — but this information may not always be up to date, causing the LLM to produce outdated or even inaccurate responses.
Conversely, RAG provides superior and more accurate answers by integrating structured and unstructured data from current external sources. This increased reliability and precision can be attributed to grounding, a vital aspect of the RAG process that enables responses generated by RAG are anchored in current external information. Grounding helps to connects abstract knowledge in AI systems to concrete, real-world examples.
External vector databases also play an important role in RAG thanks to their use of semantic keyword-assisted search. Semantic search stores embedding models (i.e., the numerical representations of text in the semantic space) to help RAG quickly identify relevant and similar content to answer user queries more effectively.
The importance of RAG in AI
RAG dramatically enhances AI capabilities beyond those of more basic generative AI models, delivering AI outputs with higher accuracy, reliability, and relevance levels to drive better user experiences. More accurate responses give developers more control over security and compliance and help protect businesses against potential risks.
Accuracy and reliability
RAG models play an important role in AI by enabling greater accuracy of outputs and helping to minimize factual errors and AI hallucinations. Because RAG pulls information from external databases and knowledge sources, RAG can generate more informative, up-to-date responses than LLMs, which are typically founded on preexisting training datasets.
RAG-generated responses can include citations so users can verify the information and do their own fact-checking. This citation availability further ensures accuracy and instills user trust in AI outputs.
Basic generative AI solutions can generate inaccurate or nonsensical outputs, known as AI hallucinations. Without proper AI model training on quality datasets, these tools can generate highly inaccurate, unreliable, and sometimes downright ridiculous AI-generated responses.
These errors erode user confidence and can lead to serious business consequences, including damaged reputations, poor decisions, or even potential legal risks. However, by grounding AI inputs on verified, recent data from external sources, RAG-generated responses can increase accuracy, reliability, and enable the information to be based on the most current data available.
Relevance and quality
Other common issues with basic AI solutions' question-answering are accuracy, relevance, and quality. Thanks to its ability to generate more accurate and contextually appropriate responses, RAG can deliver higher-quality responses.
By analyzing users’ inputs, RAG can create targeted queries to retrieve the most relevant documents and data from external sources and generate hyper-specific, customized responses. This higher-quality, more relevant information helps ensure that AI outputs more closely respond to users’ queries, improving user satisfaction.
User experience
Accurate, relevant responses are important, but users also crave enjoyable experiences when working with generative AI. For one, greater accuracy, reliability, and relevancy mean fewer errors. In addition, RAG tools that offer access to sources with links for fact-checking offers a level of transparency that users can't get with more basic generative AI solutions, further instilling confidence. Compared to more basic generative AI solutions, RAG can deliver more pleasant user experiences that foster trust and confidence.
Ensuring trusted sources back AI-generated responses doesn’t just make users feel good about your business’s AI outputs—it can also help improve workflows too. When users feel confident in their ability to trust AI outputs, they are more likely to reduce human intervention and accelerate AI implementation for greater efficiency.
Risk mitigation
It’s obvious that accurate, more reliable responses help build user confidence and drive more pleasant experiences, but they’re also key for mitigating business risks. As businesses increasingly implement AI assistants into their workflows and customer service operations, they run the risk of harmful consequences if their AI outputs include inaccuracies or outdated information.
Consider the 2024 case with Air Canada: When the airline’s chatbot gave incorrect information to a traveler, The British Columbia Civil Resolution Tribunal held Air Canada responsible and ordered the airline to pay damages and tribunal fees. In addition to financial repercussions, such an incident can damage a business's reputation and lead to a loss in customer confidence.
But RAG-generated AI responses can help. Unlike some basic generative AI tools that only pull data from an LLM’s pre-existing training data, RAG-generated AI responses source relevant, up-to-date information from external databases and knowledge sources. Since these AI outputs are able to be grounded in verified, factual data, responses can be more accurate and reliable, reducing the risk of sharing misinformation.
Control
Importantly, RAG can give developers greater control over AI applications by enhancing testing processes. By integrating an LLM’s internal training data with external databases and knowledge sources, RAG enables developers to test AI outputs against validated external sources, ensuring higher levels of accuracy.
While RAG primarily focuses on retrieving external information to enhance responses, insights from RAG can guide developers in updating the LLMs' internal data periodically.
Notably, developers can restrict which external sources RAG retrieves information from. By implementing robust encryption and data governance policies within RAG frameworks, developers can help to strengthen security and better protect against threats like data poisoning and information leakage.
How businesses are using RAG
Companies are increasingly turning to RAG to enhance processes across departments, from HR and ITSM, and key tools like enterprise search. AI tools or software leveraging RAG can help to minimize AI hallucinations and outdated policy inaccuracies, and ensure reliable and up-to-date communication. In HR, RAG-integrated software helps maintain trustworthy data across systems, while in RAG helps elevate customer support through personalized and efficient responses.
Let’s explore these are the top use cases for RAG in your business:
Increasing the accuracy of AI in HR
Minimizing the risks of AI hallucinations and the use of outdated policies is particularly important in HR. HR teams are responsible for handling employees’ personal and confidential information, so all communications must be trustworthy.
Ensuring updated and trustworthy information across your imagine your HRMS, Applicant Tracking Systems (ATS,) or Employee Self-Service Portals, becomes easier using when these applications have RAG capacities – or are integrated with an enterprise-wide AI assistant that lets use your existing software while also gaining the benefits of RAG.
When employees use AI assistants with RAG, these responses can contain more relevant, up-to-date information from various external knowledge bases and databases, and they can deliver higher accuracy and reliability. This helps reduce AI hallucinations and enable HR teams to work with more high-quality, trustworthy information.
Improving visibility in ITSM
RAG supports ITSM teams by empowering incident management software to quickly retrieve and generate comprehensive incident reports and resolution steps, which could help to reduce downtime. In change management, it enhances decision-making by compiling relevant data and historical changes to assess potential impacts and risks.
For problem and service request management, RAG automates the retrieval of knowledge base articles and past resolutions, helping to streamline the identification of root causes and speeding up response times to service requests.
Perhaps that's why service desk automation has become increasingly popular in ITSM. For example, AI assistants can access various external knowledge bases and data sources, such as historical queries, past tickets, and current ticket status. Based on more up-to-date information, they can generate richer, more relevant responses.
Winning over users with personalized support communication
In addition to helping deliver more accurate, reliable, and high-quality responses, RAG can significantly improve the user support experiences across your customer relationship management (CRM) systems, help desk and ticketing systems, driving greater user satisfaction.
You could look for support tools with RAG capacities, or consider an AI assistant that integrates across your entire tech stack to provide instant and enhanced employee support.
Specifically, RAG can deliver more personalized communications for your live chat tools, and knowledge base platforms enabling you to better manage customer interactions, resolve issues efficiently, and maintain comprehensive records of support activities.
Remember that RAG can use semantic search to retrieve the most relevant information from validated external databases and knowledge sources. This enables an LLM to produce outputs founded on contextual awareness and targeted to the user's query.
The result? Faster, more reliable issue resolution for users looking for support they can count on.
Enhancing the value of enterprise search
Enterprise search leverages AI and reasoning capabilities to allow employees to quickly and easily find the information they need across various internal systems, apps, and data repositories.
You should reimagine enterprise search as more than just a basic tool for retrieving and summarizing information. Your employees aren't just searching for information from a unified search interface; they are engaging in a process of researching to find answers and gain insights.
Beyond empowering employees to speed up workflows and boost productivity, enterprise search offers granular permissions and access controls to ensure the right employees have access to the right information for strengthened security and compliance.
In many ways, RAG helps make an enterprise search engine even better. RAG-generated responses can provide links to sources for verification to minimize the risk of AI hallucinations or unclear sourcing and enable users to fact-check responses. Moveworks Enterprise Search is designed with agentic RAG to handle large, diverse volumes of data, so it's well suited to support the needs of enterprise environments, even at scale.
Limitations of RAG
While RAG can significantly improve the quality of generative AI outputs, with more accurate and reliable responses and fewer risks of AI hallucinations, AI improvements are not over.
In fact, RAG has recently undergone several improvements, including contextual document embeddings that improve its ability to understand the context of external documents during information retrieval.
Still, like all tech solutions, RAG has its limitations. Most notable is that RAG’s ability to generate accurate, reliable, and quality AI outputs largely depends upon the quality of the external data sources from which it pulls information. There's a greater risk of factual errors in the RAG-generated responses if there are errors or outdated information in these external databases or knowledge sources.
Moreover, while RAG does help prevent AI hallucinations, it's not an infallible system. If the source from which it retrieves information is incomplete, outdated, or irrelevant, it can be challenging for RAG to avoid AI hallucinations in its responses.
RAG's chief limitation can be its ability to prioritize information and understand context. Suppose conflicting information is present in its external databases. In that case, it can be difficult for RAG to determine which data to prioritize, leading to potentially redundant, conflicting, unclear, or even nonfactual responses. So how can we gain the benefits of RAG – with far fewer downsides?
Agentic RAG helps overcome the limitations of RAG
Agentic RAG is an enhanced version of RAG that integrates a more sophisticated retrieval system, advanced decision-making capabilities, and better contextual understanding. In other words, it improves basic RAG by bringing in the power of reasoning.
While basic RAG still faces challenges with AI hallucinations, accuracy, and contextual awareness, agentic RAG brings new levels of intelligence. It integrates autonomous reasoning and decision-making capabilities to help RAG find more relevant information.
Ultimately, while basic RAG holds promise, when it comes to enterprise search many are already shifting toward agentic RAG. This advanced approach enhances user experiences even more by delivering accurate, up-to-date, hyper-relevant, and personalized responses—ready to scale with your enterprise.
Get more out of your AI with RAG
Understanding RAG is key for you to optimize your AI investments across your organization. As we’ve explored, RAG boosts AI's accuracy, relevance, and quality by combining information retrieval with generative models.
Moveworks Enterprise Search leverages agentic RAG to deliver the trustworthy and accurate answers that employees are actually looking for.
Moveworks is introducing a new approach to enterprise search by enhancing RAG with reasoning. It leverages a powerful Reasoning Engine that’s able to understand employee goals, develop intelligent plans, and search across various business systems to return top-quality search results. With our entry into the enterprise search category, Moveworks’ agentic RAG is capable of:
- Understanding employees' goals by harnessing a reasoning engine to better understand user queries.
- Developing nuanced plans to fulfill those goals
- Searching across various business systems to return high-quality, accurate, and reliable results external databases and knowledge sources for high-quality, relevant results and generate responses
- Support scalability, as it can handle larger volumes of data without compromising accuracy
RAG can be a powerful solution, whether it's being used to enhance enterprise search, HR conversational AI, ITSM visibility, and personalized customer communication. By adopting RAG capabilites, your company can improve user experience, manage risks better, and ultimately help gain better control over your AI outputs.
Discover how Moveworks is bringing the power of reasoning to RAG — explore Moveworks Enterprise Search with Agentic RAG.
Table of contents
