What is Retrieval-Augmented Generation (RAG) Chatbots?

Businesses are always keen to get the best solutions to improve user interactions and solve problems. While LLMs are a better option for the everyday world of knowledge, grounded answers are the preferred choice for most business owners. Here comes to the picture— RAG chatbots.  Combined with retrieval and generation on top of LLMs, RAG chatbots drive response accuracy with expedited search and knowledge retrieval. 

The most significant draw of RAG chatbots is grounded generation or grounded content for users. By combining your company data and world knowledge with LLMs, RAG frameworks ensure you can fetch the most accurate, up-to-date, and relevant answers for your users and scale up problem-solving for your service desks.

To create a differentiated user experience for your customers and employees, you need a tool that comprehends and contextualizes queries and surfaces accurate responses to reduce wait times and accelerate response generation. RAG chatbots are way ahead of traditional chatbots, even GPT-based chatbots, in helping you empower your service desk for both customers and employees. 

In our guide, we will learn about RAG chatbots, use cases, benefits, and ways to develop retrieval-augmented generation chatbots. 

Let’s start by uncovering the fundamental knowledge about RAG. Retrieval Augmented Generation, or RAG, is an AI framework that combines traditional information retrieval systems with the generation capabilities of large language models. 

RAG AI frameworks allow you to combine your company data with large language models, refining answers with grounded content. This means your RAG applications with an LLM context window can summarize long meeting notes and improve search results, among other things.

RAG can be integrated into your existing model to improve the veracity of responses and information delivery. Regenerated Augmented Generation combines the best of both worlds through integration with traditional AI frameworks, LLM-powered engines, or proprietary datasets.

Integration of RAG systems into your LLM models can provide multiple benefits for your business. Here are some of the benefits of RAG applications. 

• Generate relevant and up-to-date information. 

LLMs are trained with limited data repositories with a specific cut-off period. This is basically the top reason why LLMs cannot surface the latest information. Regarding RAG, users can expect relevant, accurate, and business-specific information. Also, with the ability to provide facts through input prompts, you can ensure you provide the most pertinent facts to the LMMs, help with factual grounding, and produce relevant answers. 

• Facilitate vector-based search with re-rankers 

RAG allows for hybrid search, which means you can surface relevant information based on keywords and semantics. Integration with vector databases will enable RAGs to chunk data into embeddings or numerical representations to provide semantic similarity and improve search results. RAG also uses a re-ranker to allow only very few high-quality documents to be referred to. This helps determine the closest match to the query inputs and establish grounded generation. RAG improves information accuracy, relevance, and real-timeliness with vector and re-ranker search.

Retrieval augmented generation works by integrating with two systems: retrieval preprocessing and grounded generation. 

1. Retrieval—RAG pulls relevant documents from external data repositories. When a search query is provided, RAG-based applications look for relevant documents similar to the search query.  

2. Grounded generation- Based on retrieved documents that RAG finds, LLMs’ generation capabilities generate grounded answers relevant to user queries. 

These enhanced retrieval and generation capabilities help improve search performance and facilitate relevancy, accuracy, and quality of information retrieval. By integrating with LLMs agents and conversational agents, RAG can provide more comprehensive, context-aware answers to improve overall user experiences. This leads to the concept of RAG-powered chatbots.

RAG chatbots are AI-powered chatbots that aim to provide relevant and contextual answers through retrieval and generation techniques. 

Compared to traditional chatbots that refer to predefined answers, RAG-based chatbots can improve response delivery and problem resolution with grounded context generation beyond existing intent lists, offering the best use cases across customer and employee support.

Given that RAG can work with semantic search capabilities alongside reranker, RAG chatbots can work more intelligently than traditional bots and answer questions without references inside the knowledge bases.

Let’s imagine a scenario where a user asks a bot why his Figma tool is inaccessible. The chatbot then provides a step-by-step guide for resetting passwords, enabling MFA, etc. Without knowing the problem case, bot-provided answers can offer little help, which is otherwise frustrating.

However, RAG chatbots can remove this challenge. It provides more straightforward and contextual responses that help solve a user’s problem. 

For example, when a user asks the RAG chatbot the same question, it searches external systems and looks for real-time data. It then learns that Figma has a server problem and is currently inaccessible. Without misleading information, the RAG chatbots solve confusion and help users be productive. 

RAG allows LLMs to refer to external data sources and deliver better responses in a Q&A format. This is the best use case for LLM chatbots. RAG chatbots can automate various enterprise use cases and transform user experiences by providing back-and-forth answers with contextual understanding. Here are some powerful ways of using retrieval augmented generation chatbots for enterprise use cases. The best part is that you can use them for your customers and employees together. 

Every day, a business can generate thousands of employee queries. Many of them are routine. Yet, companies need to keep these queries in the queue, which costs them tremendous productivity and lost revenues. RAG chatbots with improved intent and contextual understanding can solve workplace employee support. Here is what you can do with RAG bots. 

Ask how to apply for reimbursement. 

Businesses maintain an internal expense management program for employee management and expansion. RAG bots help boost your employees' experience by giving them a better way to ask questions and get real-time solutions for reimbursement needs.  

Provide fresh updates on projects. 

Your marketing, finance, sales, operations, and facilities can have various projects that drive profitability. To keep everyone in the same loop and speed up project delivery, you can offer updates, allow your teams to work on iterations, and enhance the development cycle. RAG-based chatbots find relevant information from company-based systems and provide the right information for your people. RAG provides responsible answers, which are supported by citations. 

I found this in the Slack group channel. The team is on it. If you have any questions, contact Stuart Anderson. 

Give access to the company directory. 

Make it easier for your people to know who they reach for help. RAG chatbots simplify search functions by letting them find filters and know their roles. 

Give instant access to crucial work-related documents. 

Build a single source of truth for your people to help them find valuable resources and allow them to work efficiently. Documents such as benefits, expenses, employee handbooks, and onboarding policies are easily searchable with RAG-based chatbots. 

Delegate assignments to new hires 

Make the new hire experience more enriching with automated approvals of assignments. Instant delegation of tasks allows your new team to be engaged and loyal to your company. 

Customer service is seeing a massive transformation in the Generative AI revolution.  RAG-based chatbots make customer interactions a breeze for end-to-end resolutions. From booking flights to canceling orders, retrieval augmented generation chatbots can do more for your business to transform customer experience. Here are some of the use cases for customer support. 

Help open a bank account. 

For your mature customers who need to know about a new product offering, make it as simple as learning ABC. RAG chatbots give straightforward answers and help make fast decisions. 

Offer hyper-personalized interactions for travelers

Eliminate confusion and provide more accurate answers for your customers. Integrations with your CRM tools for RAG chatbots help you personalize chats, drive exceptional service experience, and drive loyalty. 

Automatically resolve issues with flight booking  

Let your customers have a convenient way to resolve issues faster. With rapid context understanding, your RAG chatbot can provide context-rich answers and deliver exceptional customer experience. 

Help your retail customers get the right product. 

Make sure your customers always get the proper assistance and value for their money. RAG chatbots offer expedited conversational experiences when your customers get the wrong products and help them solve the problem in real-time. 

Solve immediate delivery-related queries. 

Your customers would expect full context regarding fees, ETAs, etc., regarding cross-border delivery. RAG chatbots sync with business tools and efficiently clarify user doubts. 

Retrieval augmented generation chatbots can align with your business objectives and help you create workflows for customer support, including employee support. You can leverage endless use cases and help your users get exceptional experiences.

There are no limits on how many types of RAG-powered chatbots you can build to meet your objectives. It depends on your specific needs. Be it customer service or employee support, you choose a variety of options, but the most popular RAG chatbot can include, 

HR chatbot 

RAG-powered HR chatbots can help you build HR-related workflows to manage onboarding, onboarding, PTO management, expense management, benefits enrolment, etc.  

IT support chatbot 

Leveraging the power of RAG-powered chatbots unleashes exceptional fluidity in managing simple to complex workflows. IT support RAG chatbots can automate tasks such as password resets, account unlock, hardware troubleshooting, etc.

Knowledge management chatbots 

Let everyone work efficiently with easy access to business-related knowledge. Knowledge management chatbots help employees and customers solve critical problems by finding valuable documents such as HR, return, and Leave policies. 

Enterprise search chatbots 

From a Slack message to business system-level information, you can make every piece of information easily searchable for enhanced productivity and efficiency for your people and customers. RAG helps enterprise search chatbots improve accuracy and provide outstanding responses.   

Combining RAG with your external systems gives you many benefits as you improve accuracy and relevancy. The benefits of RAG chatbots can easily translate into better ROI value, productivity gains, and many more. Here are some of the essential benefits. 

Exceptional user experience: With grounded content generation, RAG chatbots ensure users get only relevant information, which reduces the time it takes to resolve an issue and the volume of tier-1 tickets. 

Improved ROI: As RAG transforms the existing state of automation for information discovery, it touches every value chain and improves its working process. From service desk agents to employees and customers, everyone can unlock the value of automation.  Frictionless self-service: Users can get what they need to do their work without involving a human agent. RAG chatbots are easy to use to auto-resolve problems and escalate a ticket for critical issues.

Building and implementing RAG workflows requires serious iterations, including managing multiple components such as vector databases, embedding models, building LLMs, and so forth when you look to build from scratch. It is evident that the whole project can be resource and time intensive. Let’s not forget about the need for exceptional AI expertise, which is hard to get and, of course, expensive to hire. It’s quite a heavy blow to your bottom line. Considering the time to market,  the phases from test-trial-deployment cannot be without repetitive processes due to unexpected or unintentional errors. 

For companies seeking to unleash the value of RAG chatbots in just a few months, Workativ’s Knowledge AI can help primarily. Its no-code platform brings the embedded power of LLMs, Generative AI, and retrieval and generation models within its Knowledge AI platform. It is easy for a non-technical person to get started. All the users need is to build their knowledge repositories with access to company data. It is just about uploading these knowledge articles to the platform, customizing workflows for business-specific use cases, and deploying the bot to the favorite business comms channel. It is that simple. You can start quickly and let your customers and employees benefit from RAG chatbots through rapid information search and quick solutions to problems. 

Today’s users are pretty sensitive to poor experiences. They want information instantly, and even a minute delay can lead them to seek other services. While this is true for customer support, employee experience can take a hit for a similar reason. Off-the-shelf GPT or Generative AI-powered self-service chatbots can have scalability issues when interacting with complex queries. This is where RAG-powered chatbots provide support to take customer service and employee support to the next level by removing hallucinations and ensuring relevant and accurate answers. 

Given that the employee experience is paramount to delivering exceptional customer experience, Workativ Knowledge AI helps you solve your employees’ everyday routine queries and enables them to accomplish business operations at scale. Knowledge AI encompasses the right blend of retrieval and generation engines with the power of reranker, allowing you to leverage hybrid knowledge—the best of both worlds: public and domain-specific knowledge. This ensures your employees get the correct answers, avoid misinformation, and work efficiently.  Workativ can help you leverage RAG-based chatbots for employee productivity and efficiency and deliver an outstanding customer experience in minimal time.  Schedule a demo today.  

What is a RAG chatbot?

Retrieval Augmented Generation or RAG chatbots are AI-powered chatbots built on retrieval and generation capabilities to help provide accurate and relevant answers to specific user queries. RAG uses external databases and LLMs to harness public and custom knowledge to improve information search. 

How are RAG chatbots better than traditional chatbots?

Traditional chatbots work with predefined dialog templates for known scenarios to handle FAQ-based queries. But, these chatbots cannot scale when complex queries come in. On the other hand, RAG chatbots access external company data, including public knowledge, to generate grounded knowledge that allows users to provide context-aware and comprehensive answers. 

What are the benefits of RAG chatbots?

RAG chatbots can provide many benefits for customers and employees alike. They improve productivity and efficiency by enabling quick information search, help boost self-service, save costs by auto-resolving problems, and reducing tier 1 tickets. 

What are the use cases of RAG-powered chatbots for employee support?

You can seamlessly use RAG-based chatbots for managing routine employee queries regarding password resets, account unlocks, hardware troubleshooting, password expiry alerts., etc. 

How can you avoid all the hassles of building RAG-based chatbots?

Traditional development setup requires huge investment and AI expertise, which can halt your project. With a no-code platform like Workativ Knowledge AI, you can quickly build your RAG-based chatbots by uploading your knowledge bases and turning to Generative AI answers for user queries.