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When you think of a great automated customer experience, you likely think of fast response times or a voice that sounds natural—and you’d be right. And while those aspects are important, the real key lies in how well the system understands your issue and resolves it without friction or frustration.
That level of intelligence comes from data. The best automated customer service experience is built on large language models (LLMs) trained on real customer data.
Below, we explore why data is the foundation of better conversational AI.
The role of LLMs in the customer experience
Once an AI agent has transcribed a customer’s words, it needs to understand the context behind what the caller is saying and how to respond and take action to continue moving the conversation toward a resolution.
The process of deciding how to respond and what actions to take is usually handled by large language models.
LLMs are excellent at holding natural conversations. If you’ve tried ChatGPT, Gemini, or Claude, you’ve probably been impressed with just how conversational these models can be. But when fine-tuned on real customer interactions, an LLM recognizes patterns, allowing for more natural responses and faster issue resolutions.
Why experience matters for both humans and AI
Imagine a new agent starts in your contact center. They’ve completed all of the necessary training and are familiar with the scripts. However, when customers call, their queries rarely fit neatly into those scripts.
For example, a customer might say, “Can you explain this charge?” A new agent might offer a generic policy response or ask follow-up questions that aren’t relevant. While they understand the topic, they lack the experience to immediately grasp what the customer really needs and how to get them there effectively.
Now, think about your experienced agents who have handled thousands of these calls: they can quickly recognize what the customer is really asking and can effectively decipher whether it’s about an unexpected fee, a duplicate charge, or a billing increase. They then adjust their response accordingly and diligently resolve the issue at hand.
That’s why a great customer experience starts with a data-first approach. Just like agents get better with experience, AI models similarly get better when trained on real customer interactions. The more data they learn from—how customers ask questions, show frustration, or look for reassurance—the better they get at understanding intent and responding to nuanced issues. This aspect makes conversational AI incredibly effective.
How LLMs benefit from data
If you fine-tune an LLM using customer service data like transcripts, FAQs, and customer inquiries, the model learns how to handle common tasks such as complaints, product questions, troubleshooting, and order status updates.
A fine-tuned model can:
- Better understand customer service contexts: For instance, if a customer asks about returns, it will use terms like “refund policy,” “return process,” or “exchange.”
- Generate more relevant responses: Instead of offering a generic answer, the model will respond with something more specific, like, “I can help with your return. Could you provide your order number?”
- Provide task-focused answers: The model will give concise, accurate responses, asking for needed information and staying on topic.
- Maintain the right tone: It will adjust its tone to be professional, empathetic, and responsive to customer emotions or urgency.
Why data is key in automated customer experience
Fine-tuning an LLM for customer service isn’t just about feeding it generic transcripts of past conversations. It’s about teaching a model the different ways people describe their issues when they need help.
This includes:
- How customers describe their problems: (e.g., “My internet is acting up” vs. “I have no service”)
- What questions do they ask to feel reassured: (e.g., “Are you sure I won’t be charged extra?”)
- How emotions show up in language: (e.g., “I’ve called three times already!” signals frustration)
An LLM trained on limited datasets might not recognize that these are all variations of the same issue and could respond incorrectly or, worse, send customers through unnecessary processes to get an answer.
However, a model trained on real data from millions of real customer conversations is what creates accurate and relevant answers. That way, an automated system like an AI agent can understand these variations and provide a direct, helpful answer delivered in a natural tone. That’s what turns a mundane call into a good experience.
The impact of fine-tuned LLMs on CX
Data-driven models have a clear and direct impact on customer experience (CX). Here’s are some of the benefits of working with fine-tuned LLMs:
1. Fewer errors and improved accuracy
With access to real-world conversations, data-driven models can more accurately understand customer queries. This means customers get answers that directly address their needs, not vague or generic responses. When AI gets it right, customers feel understood and their frustration dissipates.
2. Faster issue resolution
AI agents built on a fine-tuned LLM can address customer concerns without wasting time on irrelevant questions or dead ends. Whether it’s a billing question, a product inquiry, or technical support, the agent can resolve issues faster and more efficiently, reducing customer wait times and improving satisfaction.
3. More natural and personalized interactions
Fine-tuned LLMs can adapt their tone and responses based on customer intent, making interactions feel more natural and human-like. By recognizing context and past interactions, an AI agent can personalize responses, improve engagement, and build customer trust. With a deeper understanding of natural language, callers can speak freely in their own words rather than adjusting their language to what they think an automated system can understand.
4. Better handling of complex inquiries
Generic AI models may struggle with industry-specific terminology or multi-step requests. LLMs trained on relevant datasets can navigate complex customer inquiries better, providing precise and relevant solutions without the need to escalate the request to a staff member.
Why PolyAI’s data-driven approach stands out
Unlike OpenAI or DeepMind, PolyAI doesn’t focus on building general-purpose LLMs. Instead, we specialize in fine-tuning open-source models for customer service.
Our models are trained on real-world conversations, which fine-tune them for precision, relevance, and tone. This enables our AI agents to understand customers, no matter how they phrase their questions. Just like human agents improve with experience, our AI-driven agents improve over time through continuous learning.
It’s not just about training on basic transcripts; it’s about capturing the nuances of human conversation to generate accurate and effective responses through multi-turn conversations. Without data-driven context, less sophisticated systems hand the call off or otherwise fail.
Choosing AI solutions with a strong data foundation
The key to effective customer service automation is an AI agent that truly understands customers. That level of intelligence comes from fine-tuning models with high-quality, real-world data. When AI is trained on real customer interactions, it delivers accurate, natural responses, reduces frustration, and resolves issues efficiently.
Speak to our team today about how PolyAI can help you implement the world’s most lifelike and adaptable AI agent to deliver effortless CX at scale.
