There are already ways of dynamically populating the context of the model with information retrieves as text embeddings from a vector store, based on the initial prompt. This way you can feed the model current and correct information which it will weave into its output

Do you have an example or a tutorial of this please?

I wrote this one a few weeks ago: https://simonwillison.net/2023/Jan/13/semantic-search-answer...

Same would be true for searching for the results or trying to find a human who is familiar. In a space where less true information is available, the results would be quite poor for literally everything.