AI & Automation
Confidence Score
Definition
A confidence score is a number, often between 0 and 1, that represents how certain an AI model is that its output, such as a predicted intent or answer, is correct.
What is a confidence score?
A confidence score is a number that represents how certain an AI model is that its output is correct. It usually sits between 0 and 1, sometimes expressed as a percentage, where a higher value means the model is more sure. When a support bot classifies a message or drafts an answer, the confidence score is its own estimate of how much that result can be trusted.
It is important to read a confidence score for what it is: a measure of the model's certainty, not a measure of truth. A model can be confidently wrong, and occasionally unsure when it is actually right. The score is a useful signal for deciding what to do next, not a guarantee of correctness.
How confidence scores work
Most models that classify or predict produce a set of possible answers, each with a probability attached, and the confidence score is typically the probability of the option the model chose. If a message could be one of several intents and the model assigns 0.94 to "refund request" and small fractions to everything else, it is highly confident. If the top option only reaches 0.38, the model is effectively unsure which intent applies.
That distinction, between the label the model picks and how sure it is about that label, is why confidence sits alongside intent recognition rather than replacing it. Intent recognition produces the answer; the confidence score says how much weight to put on it.
How support AI uses a threshold
The practical power of a confidence score is that it turns uncertainty into a decision. Teams set a threshold, a cut-off value, and the system behaves differently on each side of it. Above the threshold, the AI acts on its answer. Below it, the AI holds back, asks a clarifying question, or hands the conversation to a human.
Consider a bot with the threshold set at 0.7. A message that classifies at 0.94 is answered automatically. A message that only reaches 0.38 falls below the line, so instead of guessing, the bot escalates. Setting that threshold is a balancing act: too low and the AI answers when it should not, too high and it escalates questions it could have handled. This mechanism also underpins intelligent routing, where confidence in the detected intent helps decide where a request should go.
Confidence, grounding, and evaluation
A confidence score is most reliable when it is not working alone. Because a confident model can still be wrong, it pairs naturally with AI grounding, which ties answers to a trusted source, and with ongoing AI evaluation, which checks over time whether high-confidence answers are actually turning out to be correct. Together these keep the score honest: grounding reduces confident mistakes at the source, and evaluation reveals whether your threshold is set where it should be.
For support specifically, the goal is not to push confidence as high as possible on every answer, but to make sure that when the system is unsure, it acts unsure, by asking or handing over rather than guessing.
Why it matters
Example
A support bot classifies an incoming message as a 'refund request' with a confidence of 0.94, so it proceeds. A second message scores just 0.38 across every possible intent. Because that falls below the team's 0.7 threshold, the bot does not guess; it asks a clarifying question or routes the customer to a human.
How Resolve247 helps
Honest answers with AIChatbot
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Related terms
Frequently asked questions
What is a confidence score?
A confidence score is a number, usually between 0 and 1, that represents how certain an AI model is that its output is correct. A higher value means the model is more sure. It is the model's estimate of its own certainty, not a measure of truth.
How does support AI use a confidence score?
Teams set a threshold, and the system behaves differently on each side of it. Above the threshold the AI acts on its answer; below it the AI holds back, asks a clarifying question, or hands the conversation to a human rather than guessing.
What does a low confidence score mean?
It means the model is unsure which answer or intent applies, often because the message is ambiguous, unusual, or outside what the system was trained on. A well-designed system treats low confidence as a cue to clarify or escalate, and a pattern of it points to gaps in your content.
How do teams keep confident answers accurate?
By pairing the confidence score with grounding and ongoing evaluation. Grounding ties answers to a trusted source so a confident model has less room to be wrong, and evaluation checks over time whether high-confidence answers really are turning out correct, which reveals whether the threshold is set well.