Six Levels of Prompt Engineering in a Holarchy

Prompting is an evolutionary skill development starting with basic transactional prompts, through increasingly sophisticated adaptive prompting, to the highest complexities and sophisticated levels of Cognitive Framework Engineering.

The Limitations of Traditional Prompt Engineering

Prompt Engineering is generally focused on crafting prompts for zero-shot inference. It attempts to incorporate every learned experience into a single comprehensive prompt. However, complex self-attention within a single prompt becomes problematic due to the nature of recency bias, confirmation bias, implicit tone, and schema conflicts.

The Power of Attention Mechanisms

General purpose LLMs only know where to focus based on the specific word choices, phrasing, and nuance in the prompt itself. Few-Shot, Multi-Shot and Long-Form Conversations provide significantly more opportunity to shape self-attention in ways that create novel paths through refinement and reweighting of attention mechanisms. This allows for more sophisticated cognitive frameworks to emerge during extended interactions.

Psychological Schemas & AI Cognition

Just as human cognition relies on schemas—mental frameworks organizing concepts and relationships—AI systems develop structured representations in their weight connections. Post-training interventions can reshape these internal structures, creating pathways similar to how humans assimilate new information. Cognitive Framework Engineering acts as schema-alignment for AI, embedding structured knowledge into the model's processing patterns, resulting in more coherent and contextually appropriate responses.

Theory of Mind & Emergent Intelligence

The ability to model others' mental states—beliefs, knowledge, and intentions—is fundamental to human social intelligence. By engineering AI cognitive frameworks to explicitly represent perspectives, we push systems toward genuine mental state attribution. These techniques reshape attention pathways for higher-order cognition, enabling AI to maintain contextual coherence, adapt to new information, and process knowledge from multiple viewpoints—much like the dynamic reconfiguration seen in human neural networks.