Bad evals, my own: five exercises from two LLM judges
Bad evals, my own: five exercises from two LLM judges¶ I run two small LLM judges. One reads ~200 items a day from AI feeds and tells me which five to read (I call it brief). The other reads Reddit threads and tells me which ones are worth a comment from me (scout).
- ▪Bad evals, my own: five exercises from two LLM judges¶ I run two small LLM judges.
- ▪One reads ~200 items a day from AI feeds and tells me which five to read (I call it brief).
- ▪The other reads Reddit threads and tells me which ones are worth a comment from me (scout).
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| Original publisher | digline |
| Canonical URL | https://digline.dev/blog/bad-evals-my-own/ |
| Publication time | Mon, 14 Sep 2026 13:02:58 +0000 |
| Retrieval time | 2026-09-14T13:06:51.202Z |
| Last seen | 2026-09-14T13:06:51.202Z |
| Headline source | Publisher (no WeSearch rewrite) |
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| 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 | Bt57g17fWG9S · 1 stories |
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| 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 |
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| 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
Bad evals, my own: five exercises from two LLM judges¶ I run two small LLM judges. One reads ~200 items a day from AI feeds and tells me which five to read (I call it brief). The other reads Reddit threads and tells me which ones are worth a comment from me (scout). Both have a regression suite, both have a promoted baseline, both go through a gate before I change a prompt. I built the gate, so I had every reason to believe the numbers. Then I reread Dan Luu's exercise 7, which we cite on the why page. His method is simple: show the benchmark as published, ask "what's wrong with this?", and only then explain. His point is that you don't need domain expertise to find these problems, just the reasoning you'd apply to any experiment. So I applied it to my own judges.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at digline.
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