LLMs Diverge, Humans Converge — LLMs Can't Come Up With Ideas
Large Language Models (LLMs) tend to produce divergent outputs based on statistical patterns in their training data, which limits their ability to generate convergent, innovative ideas. Unlike humans, LLMs struggle with tasks requiring synthesis of multiple constraints, such as database design, due to biases in training data and lack of contextual understanding. Even when instructed otherwise, LLMs often revert to common patterns like short SQL aliases, showing the dominance of training data over specific directives.
- ▪LLMs generate outputs based on probabilistic interpolation of training data, leading to divergent rather than convergent thinking.
- ▪Claude Code often uses short SQL aliases like 'sd' or 'oi' despite instructions in CLAUDE.md to avoid them, showing the influence of prevalent patterns in training data.
- ▪Production-quality database schemas are underrepresented in training data, causing LLMs to default to beginner-level designs.
- ▪Database design requires understanding of access patterns, business rules, and future needs, which are typically absent from public code repositories.
- ▪Even with full context, LLMs may struggle to balance multiple design constraints effectively due to their reliance on statistical regularities.
DEV.to (Top) files mainly under programming. We currently carry 4,924 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
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 | DEV.to (Top) |
| Canonical URL | https://dev.to/nishimura/llms-diverge-humans-converge-llms-cant-come-up-with-ideas-161m |
| Publication time | Sun, 17 May 2026 07:43:46 +0000 |
| Retrieval time | 2026-05-17T08:03:59.095Z |
| Last seen | 2026-05-17T08:03:59.095Z |
| 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 | _ZAzPnbpLGU9 |
| 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
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 105282) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Satoshi Nishimura Posted on May 17 LLMs Diverge, Humans Converge — LLMs Can't Come Up With Ideas #llm #claude #ai LLMs can't come up with ideas. The output of an LLM (Large Language Model) tends to be divergent. It moves in the direction of deriving combinations from its training data. Good ideas, on the other hand, are convergent. They solve multiple problems at once with a single mechanism. When using LLMs, I think it's important to keep this difference in mind as you proceed.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).
Discussion
0 commentsMore from DEV.to (Top)
