PRISM Framework
Prompt Randomization for Increased Statistical Multiplicity
PRISM addresses a critical challenge in smaller language models: their tendency to generate similar outputs for complex prompts. By combining base prompts with RAG diversification and context enhancement, PRISM achieves 98-99% output uniqueness while maintaining relevance and quality. This makes it particularly effective for applications requiring varied responses, such as persona generation and content creation.
Percentage of unique outputs across multiple generations
Faster processing compared to traditional approaches
Maintenance of output quality across variations
Framework Architecture
Base Prompt
Core instruction set for SLM with foundational requirements
RAG Diversifier
Random context injection from knowledge base to enhance variety
Enhancement
Optional LLM processing for context refinement
Unique Output
Highly diversified response generation
Key Benefits
Dramatically improved output diversity (98-99% uniqueness)
Consistent quality across varied outputs
Reduced API costs through optimized processing
Perfect for repeated queries and persona generation
Excellent for daily content generation
Lower token usage while maintaining quality
Optimized for smaller language models (70B and under)
Enhanced performance in repetitive tasks