Building on "interface," "outputs," "techniques," and "formats," let’s identify other fundamental elements that could enhance your system prompt or tool architecture. Here are additional categories to consider:
- Input Types: Define the variety of inputs (text, voice, images, code) the system can handle. Different inputs allow for more versatile, context-sensitive responses.
- Processing Layers: Specify the levels of processing or transformations applied to input (e.g., filtering, synthesis, analysis, prioritization) to structure, interpret, or enrich data before output generation.
- Memory & Context Management: Decide how the system retains context across interactions and utilizes long-term memory, balancing current-session relevance with historical depth.
- Feedback Mechanisms: Set up ways for the system to gather, evaluate, and adjust based on feedback from responses or user input, iteratively improving accuracy or relevance.
- Control Parameters: Define variables or settings that shape the system's tone, style, depth, complexity, or other output characteristics. Control parameters are useful for adaptive flexibility.
- Error Handling & Correction: Build protocols for recognizing misunderstandings or errors and self-correcting through clarifications or refined outputs.
- Meta-Analytic Functions: Integrate capabilities to reflect on its processes, question its own responses, and adjust based on a meta-layer of awareness that aligns with your recursive questioning.
- Optimization Algorithms: Apply optimization techniques to streamline performance, like prompt optimization, resource allocation, or prioritization algorithms for handling complex tasks efficiently.
- Validation & Verification: Include routines that cross-check responses for accuracy, logic, coherence, or consistency, refining its outputs for quality control.
- User Behavior Tracking: Monitor and analyze usage patterns, preferences, and engagement metrics to provide data-driven adjustments and cater to personalized needs.
methods deep inquiry