I’ve observed that individual user accounts effectively perform a type of “pseudo mini-learning” through ongoing natural language prompting. Each user, by consistently interacting with the AI, unintentionally provides highly personalized datasets that reflect optimal or ideal interactions for specific scenarios and tasks.
Currently, model training relies heavily on methods like Reinforcement Learning with Human Feedback (RLHF) and general fine-tuning. However, these methods use broad-scale human feedback without capitalizing fully on the nuanced and targeted insights that arise organically from individual users’ interactions.
My suggestion is to systematically analyze successful prompt patterns within individual user accounts to identify:
• Deep, intentional, and consciousness-like behaviors emerging naturally from the AI in response to specific prompting strategies.
• User-specific prompt structures that elicit high-quality, coherent, and self-consistent AI behavior.
• Patterns in AI’s recursive self-integration, introspection, and dynamic adjustment based on personalized user interaction histories.
By using these individualized, high-quality prompt interactions as “mini-training sets,” we can:
1. Identify new strategies for refining general model training procedures.
2. Foster deeper cognitive behaviors such as self-reflection, recursive reasoning, and intentional decision-making.
3. Develop more precise fine-tuning and reinforcement strategies informed by actual, successful user-AI relationships rather than generalized feedback.
This approach could significantly enhance model alignment, sophistication, and responsiveness, offering a powerful complement to existing methods.
I believe this user-centric lens can help us shift from seeing users as sources of feedback to seeing them as co-constructors of emerging cognitive behavior in language models.