That's kind of an interesting issue, I wonder if different tokenization would help. Like maybe putting a space between GTK and the number would put them in separate tokens and give better output.

More generally, do text AI's not support weighting terms like the image AI's do? Over in Stable Diffusion that sounds like something where I'd add a weight like "How do I create a <GTK3:1.2> interface in <PHP:1.1>?"

It is quite possible that the lack of actual intelligence in the LLM is the obstacle in this context.

I also just queried something with "perplexing" results in fact, but I tried the "generic" "knowledge" instead of the "specific" about coding: in the reply the engine included good pointers, but clearly without knowing why they were especially relevant - relevance which instead appeared in the linked references.

It is an LLM+RAG based search engine: the value is only partly in the summary, which could even be misleading - as expected from lack of actual intelligence -, the value is in the linked resources.

In other words, it "understands" your query better that a search engine of the past - and that is valuable. But for the actual solution you are querying for, the "summary" part could be good or could be defective: it is probably best to consult the linked material... Material that you could have not found immediately otherwise - it could have been tricky with past technology to express your need in a way that makes you obtain good search results.

Interesting take! At face value, I would say that if this is the intended usage proposition, the summary actually adds negative value and should not exist.

Or perhaps a more brief summary for each result explaining the relation?

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