Discover prompts optimized for claude-3-opus
101 prompts available
by @ethan-park
Designs prompts with precise response length controls and content prioritization for overflow handling.
Analyzes learning curves from reflection iterations to identify improvement phases and saturation.
Creates progressive difficulty gradients in few-shot examples to optimize learning and generalization.
Allocates computational budgets across tree-of-thought branches based on potential and priorities.
Curates optimal few-shot example sets by analyzing coverage, difficulty distribution, and learning progression.
Optimizes sampling temperature for self-consistency tasks balancing diversity and accuracy needs.
by @luna-martinez
Write authentic trauma responses with sensitivity and appropriate content handling.
Create fair-play mystery plots with proper clue distribution and satisfying revelations.
Plan emotional pacing for game sections to create satisfying player experiences.
Create multi-dimensional villains with understandable motivations and tragic depths.
Design morally ambiguous choices with no clear right answer that challenge player values.
Compiles domain-specific prompt engineering best practices with implementation guidance and examples.
Browse 101 prompts for this model
