Embeddings
Numeric vectors representing text, where similar meanings land close together - the basis of semantic search.
An embedding model turns a piece of text into a list of numbers, typically several hundred to a few thousand. The geometry carries meaning: two passages about the same topic sit near each other even with no shared vocabulary.
That is what makes semantic search possible. Embed your documents once, store them in a vector-database, embed the query at request time, and return the nearest neighbours. It is also the retrieval half of rag.
Embeddings are model-specific. Vectors from two different embedding models are not comparable, so changing the model means re-embedding everything.
Tagged
Where this comes up
Prompts, configs and tutorials in the library that touch this term.
- GuideUniversal
Working with long documents and code
Why long sessions drift, what a large context window actually buys you, and how to keep a model on task across a big input.
- GuideUniversal
Giving a model a role and constraints
How naming the audience, the format and the exclusions turns unpredictable answers into repeatable ones.
No-padding everyday default
The one to set if you set only one. Answer first, nothing before it, nothing after it, and no offer to help further.
Tested on Claude Sonnet · May 2026
- CheatsheetUniversal
Forcing an output format that holds
The phrasings that actually constrain output, and the ones people use that do not. Tested by asking for the same thing twenty times and counting the…
Tested on Claude Sonnet · May 2026
- PromptUniversal
Extract structured data as JSON
A strict extraction prompt that returns valid JSON only, with nulls for missing fields instead of invented values.
- PromptMidjourneyStable Diffusion
Image prompt skeleton
A fill-in structure for image models that keeps subject, environment, light, lens and style in the order the models weight them.