The idea
Your files are split into passages (chunks). Each passage is turned into a list of numbers (an embedding) that captures its meaning. The store keeps those numbers so a question can be turned into numbers too and matched to the closest passages.
Why it matters
- It finds relevant text even when the wording differs.
- Only the best passages are sent to the model, which saves cost.
- Quality depends on your files: clear, well-structured text is found more reliably.
Where you will meet it
OpenAI's file search uses vector stores you create and attach to a call (file search guide). Many platforms hide it behind a Knowledge tab. Related: RAG vs fine-tuning.
What you control
Mainly the content: split large files, remove stale ones and use clear headings.
Check your GPT before you move it
Paste your instructions, file names and Actions schema into the free checker. It runs in your browser and builds a portable bundle. Nothing is uploaded.
Open the free checkerRelated guides
Pages describing other companies' products reflect their public documentation on the date shown and can go out of date.