Study Finds Generic Prompt Improvements Can Degrade LLM Performance on Specific Tasks
Researchers developed a framework called the Minimum Viable Evaluation Suite (MVES) to systematically test how prompt changes affect Large Language Model applications, finding that generic prompt additions sometimes harm performance rather than improve it. The study tested five prompt conditions across retrieval-augmented generation (RAG) and extraction tasks using open-source models, discovering that generic rules could reduce RAG citation compliance from 26/30 to 9/30 cases in some scenarios. The findings suggest developers should treat prompt modifications as potential regression risks and validate them against task-specific test suites before deployment.
Researchers from arXiv published a technical report proposing the Minimum Viable Evaluation Suite (MVES), a structured framework for evaluating Large Language Model applications at the application level rather than treating them as black boxes. The framework links application categories to specific failure modes, metrics, and validation evidence across general LLM applications, retrieval-augmented systems, and agentic workflows. Using open-source models (Llama 3 8B and Qwen 2.5 7B), the team tested five different prompt conditions with expanded test suites of 30 cases each. The results revealed a counterintuitive finding: generic prompt improvements do not consistently enhance performance. While stronger output-contract prompts improved strict extraction tasks for both models, the same generic rules degraded RAG citation and content-compliance performance, with the most dramatic decline occurring in Qwen 2.5 on RAG tasks. The researchers conclude that prompt engineering should follow an evaluation-driven approach, treating all prompt changes as potential regression risks that require validation against task-specific test suites before production deployment.
What's missing
The study's limitations include evaluation on only two relatively small open-source models (7B-8B parameters) in local conditions; generalization to larger proprietary models (GPT-4, Claude) or cloud-based deployments is unclear. The paper does not discuss how results might differ with different evaluation metrics, larger test suite sizes, or different types of generic prompts beyond those tested. The reproducibility of findings across different hardware configurations or with model updates is also not addressed.
What different sources said
- arXiv cs.AICenter
When Generic Prompt Improvements Hurt: Evaluation-Driven Iteration for LLM Applications
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