Embeddings
Lists of numbers that stand for text, built so that similar meanings end up close together. Semantic search runs on them.
An embedding model turns a piece of text into a list of numbers, anywhere from a few hundred to a few thousand. Meaning shows up as geometry: two passages on the same topic sit near each other even when they share no words.
That is how semantic search works. Embed your documents once and store them in a vector-database. At request time embed the query and return the nearest neighbours. It is also the retrieval half of rag.
Each embedding model has its own space. Vectors from two models cannot be compared, so switching models means embedding everything again.
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Where this comes up
Prompts, configs and tutorials in the library that touch this term.
- GuideUniversal
Turn a messy task into a prompt in five minutes
Most weak prompts come from a task nobody fully described. A five-step routine to fix that before you type, with a worked example.
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Summaries you can trust
A summary is a claim about a document, so it can be wrong in the same ways as any claim. Four habits keep it honest.
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Briefing a coding assistant: the first message
A coding assistant works from what you tell it and what it can see. Here is what to put in the first message so you spend less time correcting it.
- GuideUniversal
How to check what an AI tells you
A few checks that catch most wrong answers: which claims to verify, how to ask for sources, and why a confident tone tells you nothing.
- CheatsheetChatGPT
ChatGPT commands and keyboard shortcuts
The commands and shortcuts the interface never shows you, in one table. Mostly keyboard, mostly undocumented.
Tested on GPT-5 · May 2026
- GuideUniversal
How to write a prompt that actually works
A short checklist to run any prompt through before you send it: who, what, shape and what to leave out. Those four things separate a specific prompt…