Revision Prompting: improves industrial LLM processes
The Problem that Revision Prompting solves We prompt LLMs in two ways: Ad-hoc prompting Prompts LLMs manually, with a custom instruction per call. Examples Asking a coding agent to implement a new feature. Industrial prompting Prompts LLMs as part of an automated process, with the same instruction across calls.
- ▪The Problem that Revision Prompting solves We prompt LLMs in two ways: Ad-hoc prompting Prompts LLMs manually, with a custom instruction per call.
- ▪Examples Asking a coding agent to implement a new feature.
- ▪Industrial prompting Prompts LLMs as part of an automated process, with the same instruction across calls.
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Story provenance
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Record
| Original publisher | Revisionprompting |
| Canonical URL | https://revisionprompting.info/ |
| Publication time | Wed, 12 Aug 2026 11:58:05 +0000 |
| Retrieval time | 2026-08-12T12:11:35.809Z |
| Last seen | 2026-08-12T12:11:35.809Z |
| 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 | Ws0I5mWpXtaM · 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 |
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
The Problem that Revision Prompting solves We prompt LLMs in two ways: Ad-hoc prompting Prompts LLMs manually, with a custom instruction per call. Examples Asking a coding agent to implement a new feature. Asking a chatbot to draft an email. Industrial prompting Prompts LLMs as part of an automated process, with the same instruction across calls. Examples Extracting structured information from invoices as part of an accounting pipeline. Translating documentation pages into other languages as part of a release process. Industrial prompting typically processes some Input data with an Instruction to produce some Output. Whenever the Input gets updated, industrial prompting naively re-runs the Instruction on the UpdatedInput to produce the UpdatedOutput.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Revisionprompting.
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