The Problem is Prompt Debt: You can't be model agnostic and hand-tune prompts
The article introduces the concept of "prompt debt," arguing that hand-tuning prompts for specific large language models undermines the goal of being model-agnostic. When organizations carefully craft prompts to exploit a particular model's quirks, they create technical debt that makes it difficult to switch models or adapt to updates.
Background
- Drew Breunig is a technologist and writer focused on AI, product design, and data science, known for thinking critically about how LLMs are actually used in practice.
- "Prompt debt" is a term Breunig coins to describe the accumulating cost of hand-crafted prompts that work for one specific model but break or need rewriting when the underlying model changes (e.g., an upgrade from GPT-4 to GPT-4o, or switching from OpenAI to Anthropic).
- The piece argues against the common advice to be "model agnostic" — if you finely tune prompts to a particular model's quirks, you become dependent on that model. True model-agnosticism would require avoiding such fine-tuning, but that trades performance for portability.
- The concept mirrors "technical debt" in software engineering: short-term convenience (hand-tuned prompts) creates long-term maintenance burden and lock-in.
- This matters because companies and developers increasingly build products and workflows around LLMs — if prompts are brittle, every model update or provider switch becomes expensive and risky.