Evolutionary Data Making – How to train embedding models
The article discusses the development of a new method for acquiring training data for embedding models used in search systems. By employing evolutionary principles, the authors created a system that refines data generation policies to improve search results. This approach led to significant performance improvements in their model, demonstrating the importance of data generation processes in machine learning.
- ▪The new data acquisition method is inspired by evolutionary principles.
- ▪A 0.6B parameter model trained on this data improved search performance by 37%.
- ▪The methodology for generating high-quality retrieval training data remains largely unshared among leading providers.
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Story provenance
Source · retrieval · rights · ranking — open for full record
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Wafer |
| Canonical URL | https://wafer.systems/blog/edm/ |
| Publication time | Thu, 21 May 2026 15:54:56 +0000 |
| Retrieval time | 2026-05-21T16:01:31.036Z |
| Last seen | 2026-05-21T16:01:31.036Z |
| 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 | 4LhSqdDO8GbZ |
| 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 |
Rights status (four layers)
WeSearch handling by dimension
| 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
← Blog Evolutionary Data Making Chris Gresla 2026-03-17 TLDR We needed a better way to acquire training data for the embedding model that powers search on our phone OS. Static data generation methods rely on heuristics to pair queries with relevant documents, capturing obvious associations but failing to scale or find nuanced data. Inspired by the principles of evolution, we built a search system where frontier LLMs explore, grade, and refine data generation policies, guided by a constitution of quality principles we call The Good Data Manifesto. A 0.6B parameter model trained on this data improved NDCG@10 by 37% and won or tied 82% of blind head-to-head comparisons on real user queries.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Wafer.
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