The Gist
Old metrics fail. Traditional CX metrics miss tone, accuracy and customer trust in AI interactions.
Auto evals needed. Auto evaluations provide scalable, detailed checks on AI responses for safety, tone and grounding.
Framework for improvement. The EVALS+ Pyramid gives CX leaders a structured approach to measure and improve AI outputs.
Generative AI has stepped in to handle tasks that people used to do. It’s answering customer questions, suggesting products and writing emails on behalf of brands. While it’s impressive, this shift exposes a problem no one fully prepared for. The old ways of measuring customer experience don’t cut it anymore.
As Andrew Ng wrote, “A barrier to faster progress in generative AI is evaluations, particularly of custom AI applications that generate free-form text.” Put simply, you cannot improve what you are not measuring, and most companies do not have the systems in place to measure the right thing.
Table of Contents
The Blind Spots in Traditional CX Metrics
CX teams have long leaned on metrics like CSAT, NPS, and AHT for years. They track big-picture trends and basic operational efficiency. But they miss the nuances of AI-powered conversations.
Picture a chatbot that closes a ticket fast. Was it polite? Did it make up a policy? Did it feel like something your brand would actually say? Did it confuse the customer? Traditional metrics leave those questions unanswered.
That is where auto evaluations, or auto evals, come in. They dig into the details. This means not just how quickly something was handled, but whether the response made sense, stuck to facts, used the right tone and actually helped the customer. They provide a nuanced, scalable way to judge how AI systems behave in real-world scenarios, not just whether they responded.
Related Article: Top Customer Experience Metrics That Matter Today
What Auto Evaluation Actually Measures
Auto evaluations go beyond accuracy. They function as a continuous quality control layer and ask about five key factors.
Clarity: Was the response understandable and complete?
Helpfulness: Did it address the user’s problem or dodge it?
Grounding: Were facts drawn from reliable sources?
Tone: Was the AI empathetic, appropriate and on-brand?
Safety: Did it avoid hallucinations, bias or risky outputs?
This level of evaluation is critical in customer-facing contexts. A wrong answer is one thing. But an unsafe, off-brand or biased one can damage trust instantly. Take the example of an online retailer that uses AI-generated product descriptions. By using auto evals, they can flag cases where luxury handbag listings sound too casual or off-brand and fix them, which can improve their click-through rates.
A Practical Framework for Evaluating AI Content
Auto evals are not one-size-fits-all. To make them effective, companies need a structured, scalable approach. That is why I developed the EVALS+ Pyramid model, a six-layer framework built from industry best practices, research and enterprise experience.
E: Establish the Right Metrics
Start by defining quality in your context. Use a blend of the following elements. Automatic scores, such as ROUGE (recall-oriented understudy for gisting evaluation), BLEU (bilingual evaluation understudy), helpfulness and hallucination rates, provide structured benchmarks. Heuristic signals, like verbosity, evasiveness and toxicity, offer further guidance. Human scores, including clarity, tone and satisfaction, add valuable subjective assessment. Outcome-based metrics, such as resolution rate and deflection rate, show real-world impact. And safety and compliance checks help catch policy violations or unsafe outputs.
V: Validate Real-World Scenarios
Create a diverse scenario bank that includes common and long-tail queries, adversarial and edge-case prompts and different user personas (i.e., new user vs. repeat user). It should also include incomplete, multilingual or noisy inputs as well as “don’t-know” behavior and fallback testing.
AI should be evaluated the way real users behave, not in idealized test cases.
A: Automate Pipelines and Feedback Loops
Manual reviews cannot scale. Automate your evaluation stack to run tests during each deployment (CI/CD), compare model versions side-by-side and integrate evals with prompt tuning and retraining workflows. It should also apply to structured, unstructured and multimodal AI outputs. Crucially, even with automation, integrate human-in-the-loop spot checks. While automated systems are efficient, human oversight remains vital for nuanced qualitative analysis that automated metrics might miss.
This creates a closed-loop system for continuous improvement.
Related Article: Leading Brands Speak Out: You Need to Balance AI and the Human Touch
L: Localize and Personalize
AI must work for all users. Evaluation should cover different languages, geographies and demographics. It should support personalized content across user profiles and maintain fairness across gender, race and ability. Accessibility for users with language or cognitive challenges must be considered, along with modality-specific performance, such as images, speech and documents.
Good AI is not just accurate; it is inclusive, adaptable, and fair.
S: Systematize Governance and Visibility
Move evaluation beyond tech teams by aligning eval metrics with CX and business KPIs. Build dashboards for internal transparency, and establish cross-functional oversight involving product, legal, CX and compliance teams. It is important to track model lineage, eval history and audit trails, and to embed eval requirements into model and vendor contracts.
Governance means that evaluations support accountability and scale.
+: Data, Drift and Model Comparisons
The “+” represents critical support structures that strengthen your strategy. This includes data quality checks on eval and prompt datasets, drift monitoring to catch regressions over time and vendor or model benchmarking before deployment.
Without these, even the best evaluation metrics can become unreliable.
How Companies Are Updating Their AI Evaluation
Many organizations are already evolving their evaluation strategies. LLM-as-a-judge setups, where one model grades another, are gaining popularity. Human-in-the-loop spot checks help fine-tune tone and edge cases. Custom checklists measure brand consistency and policy adherence. Benchmarks like MT-Bench, HELM and TruthfulQA are becoming industry standards. Open-source tools like RAGAS and Deepchecks help teams integrate quality signals into pipelines.
Why This Is Urgent for CX Leaders
Customer experience is where AI meets real people, and people notice when things go wrong. They pick up on a robotic tone in sensitive situations, inaccurate policies that cause confusion and biased answers that exclude or offend.
Auto evals give you control. They provide early warning systems, continuous feedback and clear direction for where to improve. They let you track progress, not just precision.
The ROI of Auto Evals
Auto evaluations are a smart investment. They help catch issues like hallucinations or off-brand replies early, which means fewer escalations and lower support costs. More importantly, better AI responses lead to happier customers, stronger brand trust and higher loyalty. Think about the savings from fewer customers churning due to bad AI, or the extra revenue from helpful product suggestions that actually convert.
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Measurable Progress, Not Just Buzz
If generative AI is already in your customer workflows, then auto evals must be too. They are not a luxury or a nice-to-have. They are the foundation of safe, helpful and trustworthy AI at scale.
The smartest CX teams are not just deploying AI. They are measuring, monitoring and improving it, one eval at a time.
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