How are confidence scores calculated?

It depends on the stage. Intent classification confidence typically comes from the probability distribution over intent classes. Retrieval confidence comes from embedding similarity scores. Response confidence is often estimated by checking how much the response is grounded in retrieved content versus the model\'s own knowledge.

What\'s the right confidence threshold for customer support?

There\'s no universal number — it depends on the cost of a wrong answer in a given context. For low-stakes informational queries, 0.7 might be acceptable; for actions like issuing refunds, you\'d want 0.95+. Calibrate thresholds based on your own conversation audits.

Can confidence scores be gamed by adversarial customers?

Confidence scores reflect the AI\'s internal certainty, not customer input validation. A customer can\'t make the AI more confident by rephrasing a question — confidence is a property of the model\'s output distribution, not a function of how assertive the customer is.

Glossary

Confidence Score

A confidence score is a numerical measure (typically 0–1) that represents how certain an AI system is about a given output — such as an intent classification, a retrieved document\'s relevance, or the accuracy of a generated response — used to gate whether the AI proceeds, asks for clarification, or escalates to a human.

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What it means

Key insight

An AI that knows what it doesn\'t know is far more valuable than one that always answers — confidence scores are how that self-awareness is operationalized.

Confidence scores quantify uncertainty at key decision points in an AI support system. During intent detection, a confidence score indicates how clearly the customer\'s goal was understood. During retrieval, relevance scores indicate how well the retrieved documents match the question. At response generation, confidence can be estimated by how much the response is grounded in retrieved content versus relying on model priors. These scores are then compared against configurable thresholds: above the threshold, the AI proceeds; below it, the system takes a safer action — asking a clarifying question, surfacing a generic fallback, or routing to a human agent. This is the primary mechanism by which AI support systems avoid confidently wrong answers in edge cases.

Why it matters

For Shopify merchants, confidence-gated routing is what makes AI support trustworthy at scale. Without it, the AI would attempt to answer every query regardless of certainty, producing confident-sounding hallucinations for edge cases it actually knows nothing about. With properly set confidence thresholds, the AI handles what it knows well autonomously and surfaces everything else to human agents — which keeps customer satisfaction high and prevents the support errors that erode trust.

How Bookbag helps

Threshold Configuration

Merchants can set confidence thresholds per topic area — a lower threshold for high-stakes topics like refunds (more likely to escalate) and a higher threshold for informational queries — balancing automation rate with quality.

Confidence Visibility

Bookbag\'s agent dashboard shows confidence scores for recent conversations, helping teams identify which topic areas have systematically low confidence and need knowledge base reinforcement.

Graceful Uncertainty Handling

When confidence falls below threshold, Bookbag delivers a natural-sounding fallback — not an error message — and seamlessly transitions to human support or a clarifying question.

Frequently Asked Questions

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