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Make Customer Service Reps Smarter with RAG (Retrieval-Augmented Generation)

Today's AI copilots support contact center agents with dialogue suggestions, behavioral cues, and surfacing relevant knowledge. However, these systems often face challenges in delivering accurate and contextually relevant responses. Retrieval-Augmented Generation (RAG) addresses these challenges by combining the precision of retrieval-based models with the creativity of generation-based models. 

What is Retrieval-Augmented Generation (RAG)?


RAG is a hybrid approach that enhances AI copilots serving contact center agents. It retrieves relevant information from a large corpus (like a knowledgebase) and generates coherent and contextually appropriate responses using the retrieved information, leveraging both retrieval system knowledge and generation model fluency.


For example, during an EV charger customer query, a contact center agent might not know if a particular EV charging unit works in a specific higher altitude location like Flagstaff, Arizona. RAG can instantly surface a precise answer by scouring knowledgebases and documents: "The charger operates in temperatures from -22°F to 131°F (-30°C to 55°C) and at altitudes up to 6,600 ft (2,000m), which covers Flagstaff. It's rated for harsh weather conditions like floods, heavy rains, storms, and snow, making it suitable for Flagstaff's environment."


Advantages of RAG for enhanced knowledge surfacing


  • Real-time data access: Retrieves the most current information, ensuring up-to-date knowledge.

  • Comprehensive responses: Integrates multiple data sources for thorough and detailed answers.

  • Contextual accuracy: Pulls relevant information based on the specific query context.

  • Scalability: Handles a large volume of queries simultaneously, ideal for large-scale support.

  • Customizability: Tailors responses with specific data relevant to different users or use cases.

  • Efficient resource use: Saves time and computational resources by avoiding retraining.

  • Model integrity: Keeps the core model unchanged, retaining general-purpose capabilities while leveraging external data.


How about the Fine-Tuning approach?


Fine-tuning involves further training a pre-trained LLM on domain-specific data to enhance performance for specific tasks. For example, fine-tuning a sales AI copilot on high-performing cold emails or a customer service AI copilot on transcribed call audio. Where Fine-Tuning shines is in tailored responses, consistent performance, and less dependency on external data. The problem organizations will find is that Fine-Tuning is time-consuming due to data preparation and resource-intensive.


Key differences between RAG and Fine-Tuning


Data usage and implementation are the two areas where RAG and fine-tuning differ. RAG uses external data at runtime, keeping model parameters unchanged. Fine-Tuning modifies model parameters with domain-specific training data.


When it comes to implementation RAG requires continuous data retrieval and prompt injection, suitable for dynamic updates. Fine-Tuning occurs before deployment, producing predictable results but requiring extensive training data.


Lastly RAG can undergo complex integrations, potential latency, and continuous maintenance which can be challenging for enterprises, however, the benefit of real-time updates far outweigh any of those hurdles.


Understanding these methods helps you choose the best approach for your AI copilot. Minerva CQ offers both RAG and fine-tuning solutions, but the dynamic needs of its clients often make RAG the preferred choice due to its real-time adaptability and ability to provide contextually relevant information.


How do you get started with implementing RAG in customer support


Implementing RAG in customer support can improve how businesses handle customer queries by providing precise, contextually relevant information to agents in real-time during a conversation with a customer. However, the transition to a RAG-powered AI copilot requires careful planning and execution. To get started, you need to follow a structured approach that includes several critical steps: data collection and preparation, model selection, system integration, testing and evaluation, and continuous improvement. Each step ensures that the RAG system is robust, efficient, and capable of meeting dynamic customer needs.


  1. Data collection and preparation: Gather and preprocess a comprehensive knowledge base, including FAQs, user manuals, and past customer interactions.

  2. AI copilot selection: Choose an AI copilot that provides immediate value to your agents and helps you achieve improvements in KPIs such FCR, AHT and CSAT. These AI copilots should also have appropriate retrieval and generation models and a supporting vendor team that can fine-tune them with domain-specific data.

  3. Integration with existing systems: The AI copilot now needs to integrate the RAG system with current customer support platforms.

  4.   Testing and evaluation: Test the RAG system under various scenarios to evaluate its performance. Delve deeper into the agent experience and how they leverage RAG, whether it’s embedded in the experience and knowledge surfaces automatically or if it’s via search or both.

  5.   Continuous improvement: Regularly update the knowledge base to adapt to new information and customer needs and consistently improve the agent UI.


The future of RAG in customer service

As AI technology advances, RAG systems will offer even more sophisticated and personalized customer service solutions. Future developments may include:

  • Multimodal RAG systems: A picture can be a thousand words. As we look to the very near future integrating text, voice, and visual data will provide better support.

  • Adaptive learning: Enhancing RAG systems with real-time learning capabilities provides a level of sophistication and responsiveness that will give companies an advantage.

  • Emotional intelligence: Developing RAG systems that understand and respond to customer emotions.


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