Prompts Aren't Real
Well one way would be to just mash the prompt with your hands and hope for the best. We can make a machine mash the prompt with its hands instead.Once we have pass^k tests, we’ve got a repeatable measure of how well the prompt works. This is enough for us to hook our prompt up to an optimizer, like genetic pareto (GEPA) in this example.The idea here is that an algorithm with an LLM in it can reflect on why a prompt did well or poorly on our test suite, and then it can attempt modifications to the prompt.
- ▪Well one way would be to just mash the prompt with your hands and hope for the best.
- ▪We can make a machine mash the prompt with its hands instead.Once we have pass^k tests, we’ve got a repeatable measure of how well the prompt works.
- ▪This is enough for us to hook our prompt up to an optimizer, like genetic pareto (GEPA) in this example.The idea here is that an algorithm with an LLM in it can reflect on why a prompt did well or poorly on our test suite, and then it can a
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Record
| Original publisher | Evaluation |
| Canonical URL | https://evaluation.club |
| Publication time | Sun, 20 Sep 2026 15:59:25 +0000 |
| Retrieval time | 2026-09-20T16:58:46.650Z |
| Last seen | 2026-09-20T16:58:46.650Z |
| 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 | -Hp5tCKDuNNM · 1 stories |
| 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
So how do you improve from that baseline? Well one way would be to just mash the prompt with your hands and hope for the best. But there’s a better way. We can make a machine mash the prompt with its hands instead.Once we have pass^k tests, we’ve got a repeatable measure of how well the prompt works. This is enough for us to hook our prompt up to an optimizer, like genetic pareto (GEPA) in this example.The idea here is that an algorithm with an LLM in it can reflect on why a prompt did well or poorly on our test suite, and then it can attempt modifications to the prompt. Automatically, without our intervention.
Excerpt limited to ~120 words for fair-use compliance. The full article is at Evaluation.
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