Revolutionizing AI customer support with RAG chatbots 

Contributing experts

This post is part two of a three-part series about the use of retrieval-augmented generation (RAG) in AI models. Part one explored how RAG streamlines knowledge management systems to improve access to accurate and reliable information. 

Any consumer who has successfully interacted with a chatbot knows firsthand that artificial intelligence (AI) is transforming customer support.  

Today’s AI-powered chatbots use techniques like rule-based systems, machine learning (ML), and natural language processing (NLP) to help customers cancel orders, schedule appointments, or update account information. According to a recent Hubspot report, 47% of companies use AI chatbots to handle customer support. 

But while traditional chatbots have proved effective for basic customer requests, the technology still has notable drawbacks.

Chatbots trained on predefined customer support scripts or outdated FAQs often produce generic and inaccurate responses. This static training hinders the tool’s ability to adapt when product information changes.

Additionally, traditional chatbots can lose track of conversations due to memory limitations and reliance on basic natural language understanding (NLU) models.

Download our white paper, “Improving customer experience to accelerate business growth” to learn about the importance of emotional rapport, data, and analytics when developing a CX strategy. 

Optimizing outputs with retrieval-augmented generation 

To address this context-awareness problem (and other AI chatbot shortcomings), companies are supplementing their chatbots with retrieval-augmented generation (RAG). RAG is an advanced AI technique that combines real-time information retrieval and natural language generation (NLG).  

Unlike traditional chatbots, RAG systems retrieve up-to-date information from a verified database, which is then sent to a generative AI (GenAI) model to generate an answer for the user. This process allows RAG-powered chatbots to excel at accuracy, problem-solving, and personalization, which leads to higher customer satisfaction and retention rates. 

In this blog, we’ll explore how organizations can leverage RAG benefits to enhance chatbots and shape the future of AI-powered customer support. 

Benefits of using RAG in AI customer support chatbots 

More accurate responses 

Before generating a response, RAG chatbots reference current information from authoritative external sources, including a company’s updated knowledge management system (KMS). This ensures that the chatbot’s GenAI response is cross-referenced and grounded in verified data.   

For instance, when a customer asks about a retailer’s return policy, a RAG chatbot will generate a response based on the most accurate and up-to-date information retrieved from a trusted database. 

In contrast, a traditional chatbot may provide outdated information for the same query. Why? Traditional chatbot responses are based on pre-trained knowledge and fixed data sets that may contain expired information or even hallucinations (fabricated answers based on patterns or limitations in the AI model’s training data).  

Error-prone chatbots can frustrate a company’s internal stakeholders and turn customers away. RAG’s ability to consistently pull information from trusted knowledge sources helps earn customer trust and satisfaction. 

Air Canada recently learned a hard lesson about traditional chatbot inaccuracies when a customer who was given erroneous bereavement fare information by the airline’s chatbot and then charged full price sued the company. The airline damaged its reputation when it distanced itself from the error by claiming the chatbot was “responsible for its own actions.” 

Discover essential strategies for preventing AI hallucinations. Download our white paper, “When machines dream: Overcoming the challenges of AI hallucinations” and learn how to build customer trust with reliable AI outputs. 

Improved contextualization 

Another strength of RAG chatbots is the ability to access and use a customer’s entire conversation and purchase history when searching for relevant information during customer interactions. 

Here’s a hypothetical exchange between a customer and RAG chatbot showing the power of context-awareness: 

Customer: Can you check on the status of the sneakers I purchased last week? They should have arrived by now.  

RAG chatbot: Sure, I can help you with that. I see your order was shipped on March 2 and is delayed because of weather conditions. It’s expected to arrive by March 8.  

Customer: What if my order doesn’t arrive by March 8? 

RAG chatbot: If your order is late, you will be eligible for a full refund or a replacement. I can assist you with either option if needed.  

In this example, the RAG chatbot uses context from the customer’s order history and tracking system. It also dynamically retrieves the shipping status and refund policy to provide accurate responses.  

Most traditional chatbots would struggle to complete this multi-step scenario due to their reliance on predefined rules. Deviations in the conversational flow may cause a traditional chatbot to produce inaccurate outputs or lose context and annoy the customer with redundant questions.  

Decrease in operational costs 

With RAG chatbots handling routine and multi-step queries and providing 24/7 customer support, companies can operate with leaner support teams.  

According to Gartner research, by 2026, conversational AI deployments within contact centers will reduce agent labor costs by $80 billion.  

In addition to cutting labor costs, RAG’s ability to consistently deliver correct, context-aware responses reduces repeat interactions, saving time and resources. Traditional chatbots, however, are more likely to deliver inaccurate responses and force human agents to step in, costing time and money.  

The pressure to improve chatbot accuracy and reduce human support will likely increase over time. According to predictions from Zendesk, by 2030, 80% of customer interactions will be solved by AI without human interaction. 

Improved customer satisfaction 

RAG chatbots are ultimately judged by their ability to improve metrics such as net promoter score (NPS), customer satisfaction (CSAT), and customer effort score (CES). 

As we’ve discussed in this post, RAG chatbots excel at customer satisfaction tasks such as retrieving relevant, error-free answers, handling complex customer queries without involving human agents, and speeding up resolutions.   

These key RAG characteristics help reduce frustration and leave customers feeling supported and understood. Brand-new data from Salesforce confirms that US consumers used AI-based chatbot services 42% more during the 2024 holiday season than they did a year ago, helping boost online sales in the US by 4% year-over-year. 

Additionally, 80% of shoppers who have interacted with AI-based customer service report having a good experience, according to recent research by Tidio. This indicates that there is still some skepticism about AI chatbots but that most consumers are comfortable interacting with a bot for support. 

RAG-powered customer support in the real world 

While organizations use RAG chatbots across industries — healthcare, banking, hospitality — retail is arguably the most dynamic use case. Retail involves exceptionally high volumes of customer queries and high demand for post-sale support (returns, refunds, delivery issues). 

One standout example of RAG-powered customer support is Amazon’s Rufus. Rufus is a GenAI-powered shopping assistant integrated into the Amazon app and website. It answers detailed customer queries, makes personalized recommendations based on order history, and serves as a ChatGPT-like assistant for product advice. 

By leveraging RAG technology, Rufus extends beyond training data to access up-to-date information from Amazon’s product catalog to generate the most relevant responses possible.  

Amazon isn’t the only retailer using RAG for AI customer support. Walmart, Target, and Sephora — to name a few — are also utilizing RAG chatbots for real-time, personalized assistance.  

Read about how AI is a vital part of omnichannel strategies in our recent blog post about retail trends for 2025 

Two future trends in RAG-powered AI customer support 

RAG and multimodal AI 

Multimodal AI simultaneously analyzes text, images, video, and audio. RAG-powered systems can use multimodal AI and combine text-based queries with images and audio to better understand customer needs. 

For example, when a customer asks a chatbot for help assembling a new desk, the bot could provide text, images, video, and voice instructions to guide the customer through the assembly process. 

Integrating RAG with advanced audio (VoiceRAG) 

VoiceRAG is a real-time speech-to-speech capability that combines Azure OpenAI’s GPT-4o Real-Time API with Azure AI Search. It enables natural, voice-based interaction grounded in up-to-date information. 

This trend is a spinoff of multimodal AI where RAG’s integration with audio is so refined that consumers can communicate with chatbots in a near-human way.  

In a customer support environment, VoiceRAG can power chatbots to deliver immediate and accurate vocal responses to customer inquiries, streamlining communication and speeding up resolutions.

The dynamic and personalized future of customer support

With its ability to generate context-aware responses based on verified data, RAG chatbots address the limitations of traditional chatbots while delivering more efficient customer support. As companies integrate RAG with multimodal AI and VoiceRAG, the future of AI customer support looks increasingly dynamic and personalized.  

For enterprises looking to broaden their AI usage, HTEC is actively helping clients develop customer support systems that are ready to be integrated with the latest RAG technology.     

Ready to discover how HTEC’s AI and data science expertise can support your business strategy? Connect with an HTEC expert  

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