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Pythagora and similar frameworks are cool and very useful in the short term, but... Large-context models obviate the need for RAG and callgraph-augented generat

by ukuina 3y ago

Pythagora and similar frameworks are cool and very useful in the short term, but... Large-context models obviate the need for RAG and callgraph-augented generation. Why would would Agents need multiple turns and a framework like Pythagora in a (near-future) world with GPT4-level of capability and 10M+ token context?

senko 3y ago
The problem is not (only) the context size, it's (for lack of a better word) focus. GPT4 can easily get lost in too much information and produce results that don't work well (duplicate code or just incoherently solving a problem), and it needs a lot of handholding. Imagine GPT4 (or any other LLM) as a very eager but not very bright junior developer that just started to work with you. It's good, but it'll need a lot of situational management for it to not go wildly off the track. What improved models will bring us in the future is making Pythagora and other such tools work better for large and more complex projects. The tipping point will come when for example you'll be able to load Pythagora in Pythagora and continue development. While we do build some auxiliary/external tools with Pythagora, the core is still handcrafted mostly by a human, and I'm pretty sure that's the case with other code gen tools as well.
nsokolsky 3y ago
I've gotten access to Gemini 1.5 Pro today with a (supposedly) 1M context window and unfortunately in practice it's not good at complicated queries over such a large window, involving multiple recursive lookups back and forth.
hackerlight 3y ago
> Why would would Agents need multiple turns Because it's better. This was the insight behind AlphaGo, AlphaCode and AlphaGeometry. In Go, trying to get the entire solution in one forward pass required a 1000x larger neural net to achieve the same performance according to Noam Brown. Intelligence requires iteration, introspection, planning, sub-task breakdown, and trial and error with feedback from reality (error messages, print statements). These meta reasoning structures (whether hard-coded or, ideally, learned) will move LLMs closer to how humans reason.

by ukuina 3y ago

senko 3y ago

The problem is not (only) the context size, it's (for lack of a better word) focus. GPT4 can easily get lost in too much information and produce results that don't work well (duplicate code or just incoherently solving a problem), and it needs a lot of handholding. Imagine GPT4 (or any other LLM) as a very eager but not very bright junior developer that just started to work with you. It's good, but it'll need a lot of situational management for it to not go wildly off the track. What improved models will bring us in the future is making Pythagora and other such tools work better for large and more complex projects. The tipping point will come when for example you'll be able to load Pythagora in Pythagora and continue development. While we do build some auxiliary/external tools with Pythagora, the core is still handcrafted mostly by a human, and I'm pretty sure that's the case with other code gen tools as well.

nsokolsky 3y ago

I've gotten access to Gemini 1.5 Pro today with a (supposedly) 1M context window and unfortunately in practice it's not good at complicated queries over such a large window, involving multiple recursive lookups back and forth.

hackerlight 3y ago

> Why would would Agents need multiple turns Because it's better. This was the insight behind AlphaGo, AlphaCode and AlphaGeometry. In Go, trying to get the entire solution in one forward pass required a 1000x larger neural net to achieve the same performance according to Noam Brown. Intelligence requires iteration, introspection, planning, sub-task breakdown, and trial and error with feedback from reality (error messages, print statements). These meta reasoning structures (whether hard-coded or, ideally, learned) will move LLMs closer to how humans reason.