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With the increasing use of artificial intelligence tools in customer service, evaluating AI performance has become a strategic lever to ensure their use is reliable, responsible, and aligned with customer expectations. However, traditional evaluation methods, such as manual testing and human reviews, are showing their limits: they are time-consuming, costly, and sometimes inconsistent. In this context, a new method is emerging: using one LLM to evaluate another. Known as "LLM-as-a-judge," this approach automates evaluation while enhancing the rigor, reliability, and transparency of AI-generated responses. It lays the foundation for a broader reflection on what truly constitutes trusted AI in customer service.
Large Language Models (LLMs) like GPT, Claude, or Mistral are now widely used in customer service. They power AI agents, FAQ assistants, and automated email response tools like mailbots. Their role? To understand customer queries and deliver clear, fast, and relevant responses, even outside regular business hours.
They help handle simple, repetitive requests more efficiently, guide customers to the right information, and reduce pressure on overwhelmed support teams. The result: a more responsive, always-available customer service experience, often delivering a first response within seconds.
To get the most out of these tools, it's essential to use them within a well-defined framework, supported by human oversight. LLMs already bring real value to daily operations, but like any technology, they come with limitations:
Bias: since AI models learn from large volumes of text, they can reproduce certain stereotypes or preconceived ideas. This may lead to responses that are less accurate or less appropriate for some customer profiles. Detecting these biases is essential to correct them and deliver a fairer, more inclusive, and consistent experience for all users.
Hallucinations: AI may sometimes provide answers that sound plausible but are actually false or irrelevant. This can confuse or mislead customers. To prevent this, it’s helpful to set clear rules, rely on trusted reference materials, and give customers the option to request clarification or rephrasing.
Lack of self-assessment: LLMs cannot evaluate whether their own answers are correct or useful. That’s why external evaluation systems are essential, especially in customer service, where multiple valid phrasings may exist. This requires a more nuanced approach than traditional scoring methods.
Response variability: the same LLM might give different answers to an identical question depending on the phrasing or context. While this flexibility is a strength, it can make analysis and testing more complex. To reduce inconsistencies, it's important to configure the tool properly and implement appropriate validation mechanisms.
Good customer service is no longer just about solving problems, it plays a strategic role in driving customer loyalty and standing out from the competition. In a world where consumers are increasingly demanding, every interaction becomes an opportunity. A fast, clear, and empathetic response can turn a simple request into a moment of lasting trust. A customer who feels supported won’t forget the experience, they’re more likely to come back, recommend the brand, and become true ambassadors, both online and offline.
Beyond immediate satisfaction, high-quality service helps improve the Customer Lifetime Value (CLV). It encourages repeat purchases, reduces churn, and provides valuable insights to better understand and adapt to customer expectations. Feedback becomes a powerful resource to refine offerings, evolve services, and anticipate needs.
In this context, artificial intelligence can play a key role. When integrated properly, it doesn’t replace humans, it assists them, accelerates response times, automates repetitive tasks, and streamlines the service experience. But for this to work, it must follow a customer-centric approach, where human support remains available, and accuracy and quality of responses stay paramount.
This is where the concept of Trusted AI becomes essential. To be truly effective in customer relations, AI must be reliable, easy to understand, and used responsibly and transparently. It can only enhance the experience if it is seen as fair, consistent, and genuinely customer-focused.
Trusted AI isn’t just about speed or performance. In customer service, it must meet a set of clear and essential criteria to ensure a seamless, secure, and human-centered experience. This includes:
Response reliability: trusted AI delivers accurate, clear, and context-aware answers. This strengthens the company’s credibility and reduces the risk of misunderstandings. To achieve this, it’s important to train the AI on validated knowledge bases, run regular test scenarios, and allow human agents to annotate or correct responses when necessary.
Transparency: knowing who you’re talking to changes everything. When customers are informed they’re speaking with an AI, it avoids confusion and builds trust. This can be stated clearly at the start of the interaction, along with a short explanation of what the virtual agent can and cannot do. This small step makes the exchange more honest and fluid.
Access to a human: offering a path to a human agent is essential. It’s not a failure of the AI, it’s a sign of service maturity. To enable this, simple escalation rules should be in place: detection of sensitive keywords, signs of dissatisfaction, or a direct request from the user. A smooth handoff between AI and human support is reassuring and shows that the brand remains truly available.
Data protection: customers must be able to trust that AI will handle their information responsibly. This requires strict data management: collecting only what is necessary, clearly informing users about how their data will be used, and securing its storage. It also demonstrates that the technology is being used in a responsible way and in line with regulatory expectations such as the GDPR.
Minimizing errors and bias: no model is perfect, but there are concrete ways to improve. By leveraging real-world feedback, expanding the training dataset to include diverse scenarios, and involving operational teams in the adjustment phases, organizations can reduce inaccuracies and improve response quality. A well-managed AI becomes a tool that learns and improves over time, helping deliver customer service that is fairer, more relevant, and better aligned with on-the-ground realities.
It is precisely within this logic of trusted AI that the LLM-as-a-judge approach fits. In customer service, every response matters. That’s why more and more companies are choosing to add a second layer of analysis to verify the quality of AI-generated replies. This is the principle behind LLM-as-a-judge: an AI model is trained not to respond directly to customers, but to evaluate the responses generated by another model.
This approach helps to enhance consistency, improve the reliability of automated interactions, and better control AI-driven communication on behalf of the company.
This "AI judge" can, for example:
-compare several phrasings and select the one that is clearest or most useful for the customer
-automatically detect errors, contradictions, or vague wording
-provide a justification or a confidence score for the delivered response
Some companies, like DialOnce, go even further by implementing daily performance monitoring of their AI agent, based on three key indicators: resolution rate, customer satisfaction, and response compliance.
To do this, a dedicated LLM reviews a sample of conversations each day, automatically assigning labels such as “solution_proposed” (request handled) or “good_mood” (positive emotion expressed).
These labels are then used to calculate precise scores on service quality. This type of evaluation helps quickly identify areas for improvement and continuously refine the responses. The results speak for themselves: a 91.7% resolution rate, an average satisfaction score of 3.9/5, and a 99.6% compliance rate.
This structured monitoring shows that a well-integrated LLM as a judge can be a true lever for improving the reliability, transparency, and effectiveness of AI responses, while also strengthening user trust.
Looking ahead, developments such as multi-domain AI judges (specialized by industry), cross-model evaluation panels, or even trusted AI certification will take this further. The alliance between AI and human oversight will remain central to building customer relationships that are more robust, transparent, and sustainable.
The arrival of LLM as a judge in customer service operations doesn’t represent a disruption, but rather a logical evolution toward greater rigor and control.
It’s no longer enough to deploy a high-performing AI agent, its quality must be ensured over time, under real-world usage conditions. In this sense, continuous evaluation is becoming a new operational mindset, at the intersection of technology, human expertise, and organizational goals.
This type of approach also helps involve teams in an ongoing improvement process: user feedback, AI judge assessments, and human adjustments work together in a coherent loop. It’s this virtuous cycle that drives quality upward without adding operational complexity.
But beyond the technical aspect, this model invites us to rethink how we see AI: not as a standalone tool, but as an evolving teammate that improves through customer feedback and agent expertise. By combining human oversight, trust-based criteria, and automated evaluation tools, companies can build AI that supports a strong, fair, and lasting customer relationship.
