I don't think that's true at all. Think of it like setting up conversation constraints to reduce the potential pitfalls for a model. You can vastly improve the capability of just about any LLM I've used by being clear about what you specifically want considered, and what you don't want considered when solving a problem.

It'll take you much farther, by allowing you to incrementally solve your problem in smaller steps while giving the model the proper context required for each step of the problem-solving process, and limiting the things it must consider for each branch of your problem.

After my first day with Bard, I would have agreed with you. But since then, I've found that Bard simply has a lot of variance in answer quality. Sometimes it fails for surprisingly simple questions, or hallucinates to an even worse degree than ChatGPT, but other times it gives much better answers than ChatGPT.

On the first day, it felt like 80% of the responses were in the first (fail/hallucinate) category, but over time it feels more like a 50/50 split, which makes it worth running prompts over both ChatGPT and Bard and select the best one. I don't know if the change is because I learnt to prompt it better, or if they improved the models based on all the user chats from the public release - perhaps both.

This is just... false. Bard is not just a little worse than gpt-4 for coding, it's more like several orders of magnitude worse. I can't imagine how you are getting superior outputs from Bard.

Can you give an example of a prompt and the output for each that you find Bard to be better for?