dual-model-strategy

What problem does it solve?

This Skill addresses the challenge of building AI systems that must perform reliably on both resource-constrained local models and powerful cloud-based models, optimizing for cost and capability.

Core Features & Use Cases

• Model Capability Mapping: Understand and leverage the distinct strengths and weaknesses of different model sizes (e.g., 4B Ollama vs. GPT-5).
• Schema Design: Create Pydantic models that are compatible across model types, avoiding deep nesting and complex types for smaller models.
• Prompt Adaptation: Dynamically adjust prompt complexity and instructions based on the target model's capabilities.
• Provider Abstraction: Define a flexible system for selecting LLM providers based on task requirements and cost.
• Testing Strategy: Implement a tiered testing approach (unit, smoke, benchmarking) to ensure consistent performance.
• Cost Optimization: Strategies for using smaller models for development and specific tasks, and larger models for high-quality output.
• Fallback Chains: Implement progressive fallback mechanisms to ensure task completion even if a preferred model fails.

Quick Start

Use the dual-model-strategy skill to design a Pydantic schema for extracting entity names and types that works on both Ollama 4B and GPT-4o.