There has been steady improvement since the release of chat gpt into the wild, which is still only less than two years ago (easy to forget). I've been getting a lot of value out of chat gpt 4o, like lots of other people. I find with each model generation my dependence on this stuff for day to day work goes up as the soundness of its answers and reasoning improve.

There are still lots of issues and limitations but it's a very different experience than with gpt 3 early on. A lot of the smaller OSS models are a bit of a mixed bag in terms of hallucinations and utility. But they can be useful if you apply some skills. Half the success is actually learning to prompt these things and learning to spot when it starts to hallucinate.

One thing I find useful is to run ideas by it in kind of a socratic mode where I try to get it to flesh out brain farts I have for algorithms or other kinds of things. This can be coding related topics but also non technical kinds of things. It will get some things wrong and when you spot it, you can often get a better answer simply by pointing it out and maybe nudging it in a different direction. A useful trick with code is to also let it generate tests for its own code. When the tests fail to run, you can ask it to fix it. Or you can ask it for some alternative implementation of the same thing. Often you get something that is 95% close to what you asked for and then you can just do the remaining few percent yourself.

Doing TDD with an LLM is a power move. Good tests are easy enough to understand and once they pass, it's hard to argue with the results. And you can just ask it to identify edge cases and add more tests for those. LLMs take a lot of the tediousness out of writing tests. I'm a big picture kind of guy and my weakness is skipping unit tests to fast forward to having working code.
Spelling out all the stupid little assertions is mindnumbingly stupid work that I don't have to bother with anymore. I just let AI generate good test cases. LLMs make TDD a lot less tedious. It's like having a really diligent junior pair programmer doing all the easy bits.

And if you apply SOLID principles to your own code (which is a good thing in any case), a lot of code is self contained enough that you can easily fit it in a small file that is small enough to fit into the context window of chat gpt (which is quite large these days). So, a thing I often do is just gather relevant code, copy past it and then tell it to make some reasonable assumptions about missing things and make some modifications to the code. Add a function that does X; how would I need to modify this code to address Y; etc. I also get it to iterate on its own code. And a neat trick is to ask it to compare its solution to other solutions out there and then get it to apply some of the same principles and optimizations.

One thing with RAG is that we're still under utilizing LLMs for this. It's a lot easier to get an LLM to ask good questions than it is to get them to provide the right answers. With RAG, you can use good old information retrieval to answer the questions. IMHO limiting RAG to just vector search is a big mistake. It actually doesn't work that well for structured data and you could just ask it to query some API based on a specification of use some sql, xpath, or whatever query language. And why just ask 1 question? Maybe engage in a dialog where it zooms in on the solution via querying and iteratively coming up with better questions until the context has all the data needed to come up with the answer.

If you think about it, this is how most knowledge workers address problems themselves. They are not oracles of wisdom that know everything but merely aggregators and filters of external knowledge. A good knowledge worker / researcher / engineer is one that knows how to ask the right questions in order to come up with an iterative process that converges on a solution.

Once you stop using LLMs as one shot oracles that give you an answer given a question, they become a lot more useful.

As for AGI, a human AI enhanced by AGI is a powerful combination. I kind of like the vision behind neuralink where the core idea is basically improving the bandwidth between our brains and external tools and intelligence. Using a chat bot is a low bandwidth kind of thing. I actually find it tedious.