What do you think the best practices are to engage an AI in a collaborative dialogue in a topic where feedback or critique from the AI is recognized as valuable?

A non-exhaustive list of behaviors I consciously apply would include these items:

1. Where I'm less confident or unsure, I verbalize it.
2. I invite feedback and critique
3. When feedback is received, I try to incorporate it where reasonable, or otherwise engage with it explicitly
4. When message involves multiple components, I try to number or bullet point my items in an attempt to make my message easier for the AI to parse
5. I try to encourage a more collaborative open dialogue where the AI is treated by me as a peer or mentor.

Are there any key behaviors I'm missing, or any that you'd disagree with?

As an example of my approach in action, this is an example of an initial prompt from me in a recent conversation:

If you don’t mind indulging in speculation, I’ve been wondering what the future of AI might look like?

What I’ve been reading suggests that the current LLM approach might be nearing the limits of its effectiveness, given that we see AI creators bragging about marginal benchmark gains so I’ve been thinking about current challenges, possible lateral moves (in the absence of core technology breakthroughs) and was curious to see what you thought of them and if you would agree, disagree, or add any other items? This isn’t necessarily intended to be an exhaustive list - just what immediately came to mind when I was thinking about the topic this morning.

Limits:
1. Training data - We seem to be nearing a limit of new high quality data to use for training LLM based AI, given the recent debates around synthetic data use and how it might degrade model performance.
2. Training strategies - Currently, the training process seems to be fairly computationally expensive, and can be error prone since, as far as I can tell, there’s a lot of guesswork involved with no clear signal that a model is undertrained/overtrained without extensive testing
3. Inference cost - Especially with many current models using a monolithic model architecture, the cost of processing a user query is fairly expensive, limiting scale of deployment as models grow increasingly large

Potential Lateral Development Avenues:
1. Multi Model Approach - Instead of a single monolithic model, what a user sees as a single model might actually be a cluster of models of varying size. Depending on the evaluated complexity of user input, at the time of inference a decision is made on whether a smaller, simpler model can handle the request which might result in savings on inference cost. The potential drawbacks might be occasional bad decisions on the sub-model used, as well as a more complex training/evaluation process?
2. Increased Native Multimodal Capabilities - I wonder if we might see models that increasingly incorporate multimodal processing/output into the core model, instead of relying on external tool use? This seems to be happening already with image generation, but I wonder if we’ll see the same thing with audio processing - as opposed to the largely STT/TTS approach that seems to be currently in use?
3. Reasoning Capability - Improve robustness of model reasoning on data, potentially allowing for more accurate and nuanced outputs with (theoretically) fewer hallucinations?