--- title: Measuring an LLM’s Confidence Instead of Trusting It description: A loan-default classifier built with Google Gemini (gemini-2.5-flash) that focuses on evaluation rigor rather than just classification. The system uses... canonical: https://daily.dev/posts/measuring-an-llm-s-confidence-instead-of-trusting-it-twyzb7eja twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Measuring an LLM’s Confidence Instead of Trusting It | daily.dev og:description: A loan-default classifier built with Google Gemini (gemini-2.5-flash) that focuses on evaluation rigor rather than just classification. The system uses... og:url: https://daily.dev/posts/measuring-an-llm-s-confidence-instead-of-trusting-it-twyzb7eja og:image: https://api.daily.dev/og/posts/twyZb7Eja.png og:image:alt: Measuring an LLM’s Confidence Instead of Trusting It og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Measuring an LLM’s Confidence Instead of Trusting It **[Medium](https://daily.dev/sources/medium_js)** · 2 min read · 1 upvotes · 0 comments ## Summary A loan-default classifier built with Google Gemini (gemini-2.5-flash) that focuses on evaluation rigor rather than just classification. The system uses structured output to get a verdict, confidence score, and reason for each borrower, then routes cases via deterministic rules to auto-approve, auto-flag, or escalate to a human. Every prediction is scored against real labels using accuracy, precision, recall, F1, confusion matrix, and 95% confidence intervals — exposing the common pitfall of high accuracy masking zero default detection. The project demonstrates why measuring LLM confidence calibration matters and frames the tool as decision support rather than automated rejection. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### Why can't I just trust the accuracy score of an LLM classifier on an imbalanced dataset like loan defaults? Because a model that always predicts the majority class can score deceptively high on raw accuracy while being useless. On a lending dataset where about 21% of loans end in default, always predicting 'no default' already reaches roughly 79% accuracy while catching zero actual defaults, which is why precision, recall, and F1 matter more than accuracy alone. _Developers building classifiers like this can find deeper evaluation write-ups and comparisons on daily.dev._ ### How can I use an LLM's own confidence score to decide whether to trust its prediction? Route decisions using a deterministic rule layered on top of the model's confidence output rather than relying on the model itself to decide. In one loan-default classifier built with Gemini (gemini-2.5-flash) via structured output, a simple rule sends each borrower to auto-approve, auto-flag, or human review based on how confident the model states it is, keeping a human in the loop for uncertain cases. _Teams designing LLM-in-the-loop decision systems can track patterns like this via daily.dev._ ## Similar posts on daily.dev - [LLM Evals Are Based on Vibes — I Built the Missing Layer That Decides What Ships](https://daily.dev/posts/llm-evals-are-based-on-vibes-i-built-the-missing-layer-that-decides-what-ships-epggymxbj) · Towards Data Science · 0 upvotes · 0 comments - [When the Model Is Confident and Wrong: A Practitioner Guide to LLM Output Reliability](https://daily.dev/posts/when-the-model-is-confident-and-wrong-a-practitioner-guide-to-llm-output-reliability-thcxhmnkt) · SD Times · 0 upvotes · 0 comments - [LLMs as a Judge: How to Know if Your LLM is Healthy](https://daily.dev/posts/llms-as-a-judge-how-to-know-if-your-llm-is-healthy-iwlhxeaqe) · ByteByteGo · 1 upvotes · 0 comments - [How to evaluate LLMs before production](https://daily.dev/posts/how-to-evaluate-llms-before-production-ps4g4zhbm) · GitHub Blog · 2 upvotes · 0 comments - [How to rank fraud detection models using custom cost metrics](https://daily.dev/posts/how-to-rank-fraud-detection-models-using-custom-cost-metrics-wx70gbcru) · Red Hat Developer · 0 upvotes · 0 comments --- Tags: [#data-science](https://daily.dev/tags/data-science), [#llm](https://daily.dev/tags/llm), [#google-gemini](https://daily.dev/tags/google-gemini), [#classification](https://daily.dev/tags/classification) [View this post on daily.dev](https://daily.dev/posts/measuring-an-llm-s-confidence-instead-of-trusting-it-twyzb7eja) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Measuring an LLM’s Confidence Instead of Trusting It","url":"https://daily.dev/posts/measuring-an-llm-s-confidence-instead-of-trusting-it-twyzb7eja","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/measuring-an-llm-s-confidence-instead-of-trusting-it-twyzb7eja"},"datePublished":"2026-07-26T02:49:35.929Z","dateModified":"2026-09-14T08:41:54.484Z","description":"A loan-default classifier built with Google Gemini (gemini-2.5-flash) that focuses on evaluation rigor rather than just classification. 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On a lending dataset where about 21% of loans end in default, always predicting 'no default' already reaches roughly 79% accuracy while catching zero actual defaults, which is why precision, recall, and F1 matter more than accuracy alone. Developers building classifiers like this can find deeper evaluation write-ups and comparisons on daily.dev."}},{"@type":"Question","name":"How can I use an LLM's own confidence score to decide whether to trust its prediction?","acceptedAnswer":{"@type":"Answer","text":"Route decisions using a deterministic rule layered on top of the model's confidence output rather than relying on the model itself to decide. In one loan-default classifier built with Gemini (gemini-2.5-flash) via structured output, a simple rule sends each borrower to auto-approve, auto-flag, or human review based on how confident the model states it is, keeping a human in the loop for uncertain cases. Teams designing LLM-in-the-loop decision systems can track patterns like this via daily.dev."}}]} ```