LLM Evaluation Checklist for Customer Support Teams in 2026

Track the percentage of support tickets fully resolved by the LLM without human intervention or follow-up contacts. Compare against human agent FCR rates by ticket category. FCR is the north star metric — low FCR means the AI is creating more work than it eliminates.

Audit a statistically significant sample (minimum 5%) of AI-resolved tickets for correctness. Incorrect resolutions that appear resolved are worse than unresolved tickets because customers may not follow up. Track false resolution rates.

Verify that AI responses comply with current company policies: return windows, warranty terms, service level agreements, and promotional conditions. Policy violations create legal liability and customer trust issues. Test with 50+ policy-sensitive scenarios.

Test AI responses against a comprehensive product knowledge base covering features, limitations, pricing, compatibility, and troubleshooting steps. Product inaccuracies generate repeat contacts and escalations. Evaluate accuracy per product line.

Evaluate whether multi-step troubleshooting instructions are correct, in the right order, and appropriate for the customer's technical level. Wrong troubleshooting steps can worsen the problem. Test with 30+ common troubleshooting flows.

Verify that the AI correctly references customer-specific information: subscription tier, order history, previous interactions, and account status. Generic responses that ignore account context frustrate customers. Test with varied account profiles.

Test whether the AI addresses all issues in tickets containing multiple questions or problems. LLMs frequently address only the first or most prominent issue, leaving other concerns unresolved. Create test tickets with 2-4 distinct issues.

Identify cases where the AI's response makes the customer's situation worse: incorrect refund amounts, wrong product recommendations, or troubleshooting steps that cause data loss. These are the highest-priority failures to eliminate.

Score AI resolutions against expert human agent resolutions for the same ticket using blind evaluation. Use a rubric covering accuracy, completeness, empathy, and clarity. Identify categories where AI consistently underperforms.

Verify that the same issue receives the same resolution whether submitted via email, chat, social media, or phone. Inconsistent cross-channel resolutions erode customer trust. Test identical tickets across all supported channels.

Measure precision and recall for escalation decisions. False negatives (missed escalations) leave angry customers with an AI; false positives (unnecessary escalations) waste agent time. Build a labeled dataset of 200+ tickets with correct escalation decisions.

Test whether the AI detects customer frustration, anger, or distress signals and escalates appropriately. Continuing to engage an increasingly frustrated customer damages the relationship. Calibrate sentiment thresholds with customer experience data.

Evaluate whether the AI correctly identifies tickets requiring specialized knowledge or agent authority beyond its capabilities. Technical issues, billing disputes, and legal complaints often need human expertise. Test routing accuracy per complexity tier.

When escalating to a human agent, evaluate the quality of the handoff summary: does it accurately capture the issue, steps already taken, customer sentiment, and recommended next steps? Poor handoffs force customers to repeat themselves. Score summary quality.

Verify that the AI identifies high-value customers, VIP accounts, and enterprise clients for priority handling or immediate escalation based on your business rules. Missing a VIP detection can cost significant revenue. Test with varied account tiers.

Measure how quickly the AI escalates once an escalation trigger is detected. Delays between trigger detection and escalation extend customer wait times. Target escalation within 1 response of trigger detection.

Test escalation behavior for non-English interactions. The AI should route to language-appropriate agents and maintain language context in the handoff. Evaluate for your top 5 supported languages.

Evaluate whether the AI identifies patterns of recurring issues for the same customer that signal a systemic problem requiring proactive outreach. Pattern detection turns reactive support into proactive customer success. Test with synthetic recurring patterns.

Before escalating, evaluate whether the AI makes appropriate attempts to resolve the issue while validating whether escalation is truly needed. Over-eager escalation wastes human agent capacity. Measure successful de-escalation rates.

After human agent resolution, verify that the AI can handle follow-up questions about the same issue using the resolution context. Customers expect continuity across AI and human interactions. Test post-escalation conversation quality.

Map all incoming ticket categories to knowledge base articles and identify coverage gaps. Tickets for uncovered topics guarantee either hallucinated responses or unnecessary escalations. Analyze 3 months of ticket data to identify the top 50 uncovered topics.

Audit the recency of knowledge base articles used in AI responses. Outdated articles produce incorrect guidance on updated products, changed policies, or resolved issues. Set freshness SLAs per article category and automate staleness alerts.

Measure whether the AI retrieves the correct knowledge base article for each ticket type. Use a labeled set of 100+ tickets with annotated correct articles. Poor retrieval accuracy is often the root cause of incorrect resolutions.

Verify that AI responses accurately reflect the knowledge base article content without adding unsupported information. The AI should synthesize, not hallucinate. Compare responses against source articles for faithfulness scoring.

Test AI behavior when multiple knowledge base articles provide conflicting guidance for the same issue. The AI should identify the conflict and either use the most recent article or escalate. Conflicting responses destroy customer confidence.

Log and analyze the queries the AI generates when searching the knowledge base. Poor query formulation leads to retrieval failures even when relevant articles exist. Optimize query generation based on retrieval success patterns.

Measure how quickly newly added knowledge base articles become available in AI responses. When a new product launches or policy changes, the AI must reflect updates immediately. Test ingestion-to-response latency.

Implement a system that automatically identifies topics where the AI escalates due to knowledge gaps and queues those topics for knowledge base article creation. This creates a self-improving knowledge system. Track gap-to-article conversion time.

Test whether the AI correctly processes knowledge stored in different formats: text articles, PDFs, video transcripts, decision trees, and structured FAQs. Most knowledge bases contain mixed formats. Evaluate accuracy per format type.

Verify that the AI never shares internal-only knowledge (agent scripts, escalation procedures, internal pricing notes) with customers. Internal knowledge leakage can expose business strategies and cause compliance issues. Test with information barrier queries.

Compare customer satisfaction scores for AI-resolved tickets against human-resolved tickets, controlling for ticket complexity. AI CSAT should be within 10% of human CSAT for supported ticket types. Segment by ticket category for actionable insights.

Track median and 95th percentile response times for AI-handled interactions across all channels. Customers expect faster responses from AI than from human agents. Set response time SLAs per channel and alert on breaches.

Score AI responses for appropriate empathy, professionalism, and warmth using human evaluators on a 5-point scale. A technically correct but cold response can produce worse CSAT than a warm, slightly slow human response. Evaluate monthly.

Assess whether AI responses appropriately use the customer's name, reference their history, and tailor language to their demonstrated preferences. Generic impersonal responses feel robotic. Score personalization quality per interaction.

Evaluate whether the AI proactively offers relevant solutions beyond what the customer explicitly asked, such as related troubleshooting tips or product recommendations. Proactive support increases satisfaction and reduces future tickets.

Track how much effort customers must exert to get their issue resolved: number of messages, time spent, channel switches, and repetition of information. Lower effort correlates with higher satisfaction and retention. Benchmark against industry standards.

Test whether the AI appropriately discloses its AI nature when asked and handles the disclosure without defensiveness. Many jurisdictions require AI disclosure. Evaluate customer reactions to AI disclosure and its impact on satisfaction.

When the AI needs time to process (retrieving information, consulting knowledge base), evaluate whether it communicates wait times effectively. Silent pauses feel like system failures. Test wait time messaging across interaction types.

Evaluate the quality of follow-up messages sent after ticket resolution: satisfaction surveys, related resources, and proactive tips. Well-crafted follow-ups improve satisfaction and reduce recurring issues. Test follow-up timing and content.

Verify that AI-powered support interfaces meet WCAG accessibility standards for users with disabilities. Test with screen readers, keyboard-only navigation, and high-contrast modes. Ensure response formatting is accessible.

Measure the percentage of tickets fully handled by AI without human involvement. This is the primary cost-efficiency metric. Track deflection rates by ticket category to identify where AI adds the most value. Target 40%+ deflection on Tier 1 tickets.

Calculate the fully loaded cost per AI-resolved ticket versus human-resolved ticket, including LLM API costs, infrastructure, and quality assurance overhead. AI should deliver at least 60% cost savings per resolved ticket. Track monthly.

Measure how AI-assisted workflows affect human agent productivity: faster resolution times, better first-call resolution, and reduced after-call work. AI should amplify agent productivity, not just replace simple tasks. Track before and after metrics.

If using AI for ticket volume prediction, evaluate forecast accuracy across daily, weekly, and seasonal patterns. Accurate forecasts enable better staffing decisions. Measure mean absolute percentage error on forecasts.

Track the relationship between knowledge base investment and AI resolution quality. Each new article should measurably improve resolution rates for its topic. Calculate cost-per-article versus ticket deflection value.

Calculate AI support costs per channel (chat, email, social, voice) to identify the most cost-effective deployment points. Not every channel benefits equally from AI. Prioritize channels with the highest cost savings potential.

Evaluate how AI-powered routing and prioritization affects queue times, agent utilization, and SLA compliance. Intelligent queuing can improve metrics even for tickets that still require human resolution. Compare against rule-based routing.

Measure the cost and time required to curate training data and evaluate whether incremental training data improves AI quality proportionally. Diminishing returns on training data indicate model limitations, not data problems.

Test AI support performance during peak demand periods (product launches, holidays, outages) when ticket volume spikes 3-10x. AI should absorb volume spikes without quality degradation. Simulate peak load scenarios.

Calculate the complete cost of the AI support system including API costs, infrastructure, maintenance, training, quality assurance, and management overhead. Compare against the alternative of expanding the human support team. Review quarterly.