# Benchmarking LLMs in Real-World Applications: Pitfalls and Surprises

DevFeed: [Benchmarking LLMs in Real-World Applications: Pitfalls and Surprises](<https://devfeed.tech/articles/benchmarking-llms-in-real-world-applications-pitfalls-and-surprises-26293.md>)

Original publisher: [Read original article](<https://medium.com/feedzaitech/benchmarking-llms-in-real-world-applications-pitfalls-and-surprises-78e720d3bfa1?source=rss----e11168e7fe6b---4>)

Author: Jean Alves

Published: 2025-11-25T15:31:00Z

Content type: article

Language: en

Sources: [Feedzai](<https://devfeed.tech/sources/feedzai.md>)

Topics: [LLM evaluation / benchmarking](<https://devfeed.tech/topics/llm-evaluation-benchmarking.md>), [benchmarking](<https://devfeed.tech/topics/benchmarking.md>), [Generative AI](<https://devfeed.tech/topics/generative-ai.md>), [Security, Privacy and Abuse Prevention](<https://devfeed.tech/topics/security-privacy-and-abuse-prevention.md>)

Tags: [benchmarking](<https://devfeed.tech/tags/benchmarking.md>), [computer-vision](<https://devfeed.tech/tags/computer-vision.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [financial-fraud](<https://devfeed.tech/tags/financial-fraud.md>), [fraud-prevention](<https://devfeed.tech/tags/fraud-prevention.md>), [hallucinations](<https://devfeed.tech/tags/hallucinations.md>), [large-language-models](<https://devfeed.tech/tags/large-language-models.md>), [llms](<https://devfeed.tech/tags/llms.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [trust](<https://devfeed.tech/tags/trust.md>)

## AI overview

This article explains how Feedzai's ScamAlert uses Generative AI to identify interpretable scam signals from submitted screenshots, and why systematic evaluation and benchmarking are needed to assess consistency, explanations, performance, and limitations such as hallucinations.

## Source excerpt

By Jean V. Alves and Ferran Pla Fernández Moving beyond binary classification provides novel insights. In the real world, scams rarely present themselves in black and white. Fraudsters exploit nuance, impersonate legitimate brands, and mask malicious intent with seemingly ordinary behavior. That's why Feedzai has launched ScamAlert (patent pending), a Generative AI-based system innovating on the current paradigm of scam prevention, in response to this growing challenge. Traditional detection systems treat the problem as a binary choice: scam or not a scam, often outputting an estimated "scam likelihood" measure. This value, even if accurate, doesn't tell users why something is risky or what they should watch out for, leaving them with little guidance on how to stay safe. A potential scam SMS The binary approach can often suffer from a lack of context. While a text message may look suspicious in a vacuum (e.g., a payment request via a less safe method) the user may have other reasons to believe in its legitimacy, such as a past history of such requests. Consequently, an incorrect risk estimate based on missing context may lead users to distrust the system's abilities. A traditional binary classification system outputs only a risk estimate ScamAlert, on the other hand, makes judgements on what it knows. Users submit a screenshot of the suspected scam, and ScamAlert identifies observable red flags, patterns or behaviors that are often associated with fraud, such as suspicious links or spelling errors. This approach empowers the user with interpretable insights into the detected risk signals, instead of a vague numeric value. This places the user in the driver's seat, by presenting them with the facts and enhancing their awareness and judgment. To fully understand a systems' ability to perform this task, we pair this labelling approach with a rigorous evaluation and benchmarking protocol. We test the consistency of model outputs for the same input; the model's ability t