ai-switching-models
Switch Models Without Breaking Things
Guide the user through switching AI models or providers safely. The key insight: optimized prompts don't transfer between models (arxiv 2402.10949v2 — "The Unreasonable Effectiveness of Eccentric Automatic Prompts"). DSPy solves this by separating your task definition (signatures + modules) from model-specific prompts (compiled by optimizers).
Why switching models breaks things
Hand-tuned prompts are model-specific. A prompt engineered for GPT-4o will perform differently on Claude, Llama, or even GPT-4o-mini. Research shows optimized prompts for one model can actually hurt performance on another.
DSPy makes switching safe because:
- Signatures define what the task is (inputs, outputs, types) — model-independent
- Modules define how to solve it (chain of thought, ReAct, etc.) — model-independent
- Compiled prompts (few-shot examples, instructions) are model-specific — but re-generated automatically by optimizers
The workflow: keep your program the same, swap the model, re-optimize. Done.
Step 1: Understand the situation
Ask the user:
- What model are you using now, and what do you want to switch to? (e.g., GPT-4o to Claude, cloud to local)
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