You basically can't. We haven't developed that kind of AI. Some of us hope that just-in-time retrieval of data from an external source (usually a vector db) is

You basically can't. We haven't developed that kind of AI. Some of us hope that just-in-time retrieval of data from an external source (usually a vector db) is going to work. Like, say, "retrieve chapter 3 of Dostoyevsky's CP and make an interesting comment about it". For the record, you can have plenty of interesting angsty existential conversations with both GPTs about D right now if you so desire. Just preface your dialogue with something slightly more sophisticated than "let us have an angsty existential conversation about Dostoyevsky". This JIT retrieval might work for some cases, but my guess is it won't for large holistic pieces of work where you have to integrate and "understand" the entire edifice at once. I'm not completely sure if codebases merit such a distinction though, but you can imagine a domain like "law" might. We can push the context limit and this might bring some temporary relief, but I'm not convinced. The recent pushes to 100K seem to rely on "dropping" attention in an intelligent way. It sounds like cheating and while it might work OK for some cases, I think it'll drop the ball eventually. The integrated understanding that results from, somehow, internalizing the relation between all the hundreds of thousands of seemingly unrelated datapoints is what makes us interesting. That's also what makes an LLM interesting. It's just that an LLM only has access to that level of development when it's training. If I were to guess I think we somehow need to enable "always-training" and I'm not sure anyone has the faintest idea how. You can only go so far if you step out of school and never learn anything ever again, no matter how brilliant you are. GPT4 is quite the scholar, but we're stretching it.