The LLM Looked Smart. The Metrics Disagreed
In early 2025, a company faced backlash after offering digital bank accounts to individuals without credit. Although an AI tool was implemented to classify complaints, it struggled with recall, missing a significant number of issues. The author ultimately found that manual labeling was necessary to accurately assess the situation and improve the product's reception.
- ▪The company aimed to expand its product by offering digital bank accounts to those without credit.
- ▪Public backlash emerged as users complained about account approvals, leading to lower brand affinity.
- ▪An AI classifier showed high precision but low recall, missing many complaints.
- ▪The author manually labeled complaints to gain a clearer understanding of the issues.
- ▪Tuning the AI prompts became increasingly complex and raised concerns about overfitting.
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Record
| Original publisher | Rio |
| Canonical URL | https://tiago.rio.br/work/general/articles/llm-looked-smart-metrics-disagreed/ |
| Publication time | Mon, 18 May 2026 00:11:27 +0000 |
| Retrieval time | 2026-05-18T00:18:21.125Z |
| Last seen | 2026-05-18T00:18:21.125Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | YUcyr6QoI9g2 |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
The LLM Looked Smart. The Metrics Disagreed This case from early 2025 is an interesting reminder that, even in this brave new world where models are increasingly commoditized through transformers and LLMs, the old concepts of data science still stubbornly refuse to die. Back then, Will Bank had one clear mission: credit. That was about to change. I was hired to help expand the product by offering free digital bank accounts, even to people who wouldn’t qualify for credit. I already touched on part of this story in “An Approval Model That Finally Got Approved”, but this chapter came with an entirely different headache. Among many operational issues, there was one problem loud enough to echo through every metrics dashboard: public brand backlash.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Rio.
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