When Generic Prompt Improvements Hurt: Evaluation-Driven Iteration for LLM Applications
Daniel Commey
Why It Matters
What makes this one worth your time
Understanding that generic prompt improvements can negatively impact LLM performance is crucial for developers to avoid regression risks and ensure task-specific optimization.
Evaluation-driven prompt iteration is crucial as generic prompt improvements can sometimes degrade LLM performance.
Summary
The paper introduces the Minimum Viable Evaluation Suite (MVES), a framework for evaluating Large Language Model (LLM) applications by linking application categories to failure modes, metrics, and validation evidence. It includes a local evaluation harness for structured extraction, RAG citation/content-compliance, and instruction-following checks. The study finds that generic prompt improvements can sometimes degrade performance, emphasizing the need for evaluation-driven prompt iteration.
Key contributions
- Proposes the Minimum Viable Evaluation Suite (MVES) for LLM application evaluation.
- Develops a local evaluation harness for structured extraction and RAG citation/content-compliance.
- Demonstrates that generic prompt improvements can lead to performance declines in specific scenarios.
Notable insights
- Generic prompt additions do not always lead to performance improvements and can sometimes degrade specific tasks.
- Evaluation-driven prompt iteration is necessary to mitigate potential regression risks in LLM applications.
Possible limitations
- Not stated in the abstract
Abstract
arXiv:2601.22025v2 Announce Type: replace-cross Abstract: Evaluating Large Language Model (LLM) applications differs from conventional software testing because outputs are probabilistic, semantically variable, and sensitive to prompt and model changes. This technical report proposes the Minimum Viable Evaluation Suite (MVES), an audit-oriented structure for application-level LLM evaluation. MVES links application categories to failure modes, metrics, required artifacts, and validation evidence across general LLM applications, retrieval-augmented systems, and agentic workflows. We pair the framework with a reproducible local evaluation harness covering structured extraction, RAG citation/content-compliance, and instruction-following checks. Using Ollama with Llama 3 8B Instruct and Qwen 2.5 7B Instruct, we evaluate five prompt conditions over expanded 30-case-per-suite ablations. The results show that, in the tested local conditions, generic prompt additions do not produce monotonic improvements: stronger output-contract prompts improve strict extraction for both models, while RAG citation/content-compliance declines under some generic-rule conditions. The largest observed decline occurs for Qwen 2.5 on RAG when generic rules are appended to the user prompt, from 26/30 to 9/30. These findings support evaluation-driven prompt iteration: prompt changes should be treated as potential regression risks and tested against task-specific suites before deployment. The accompanying repository contains the test suites, prompt variants, evaluation harness, raw result logs, and scripts needed to reproduce the reported local ablations.