Prompt Engineering for Controlling LLM Behavior: A Practical Framework and Case Study Evaluation

Abstract

Large Language Models (LLMs) are highly sensitive to prompt design. We present the Prompt Effectiveness Evaluator (PEE), a modular toolkit for comparing prompt variants across tasks. We detail the architecture, metrics (ROUGE L, BLEU, BERTScore, sentiment), and contribute a new applied case study on sales projection. Using monthly sales data (Jan–Sep) and a reference projection (Oct–Dec), we evaluate three prompts—Zero shot concise, Role + constraints, and Chain of Thought (CoT). Role + constraints and Zero shot achieve the strongest overlap with the numeric reference; CoT is close but slightly lower. Results show that PEE provides reproducible, metrics driven guidance for selecting effective prompts.

Introduction

The paper presents Prompt Effectiveness Evaluator (PEE), a practical tool for systematically comparing different prompt formulations used with Transformer-based large language models (LLMs). While LLMs are powerful for tasks like summarization, translation, reasoning, and code generation, their performance is highly dependent on prompt phrasing. PEE fills a gap in existing research by providing an empirical, repeatable method to evaluate prompt variants.

PEE consists of an end-to-end system with a prompt interface, LLM query engine, evaluation metrics, and visualization dashboard, implemented using Python and Streamlit. It supports automatic metrics such as ROUGE-L, BLEU, BERTScore, and sentiment analysis. The tool allows users to author multiple prompt versions, generate multiple outputs per prompt to reduce randomness, and visualize metric comparisons through ranked tables, bar charts, and radar plots.

The study extends the original framework with a detailed sales projection case study, adding a numeric forecasting task to earlier tasks like summarization, Q&A, and sentiment rephrasing. Using a realistic sales dataset, three prompt variants—zero-shot concise, role-based, and chain-of-thought (CoT)—are compared. Quantitative results show that concise or role-specific prompts perform best for deterministic numeric outputs, while CoT yields slightly lower scores due to extra reasoning text that affects surface-level metrics.

The discussion highlights that prompt effectiveness is task-dependent: concise or role-constrained prompts excel for structured, numeric tasks, whereas CoT is more useful for open-ended reasoning. Ablation studies show sensitivity to sample count, metric choice, and prompt phrasing. Limitations include the use of a single dataset, reliance on automated metrics, and potential variations across model versions.

The work provides full reproducibility via an interactive Streamlit application and exportable JSON/CSV artifacts, enabling users to replicate and extend the evaluation pipeline.

Conclusion

PEE operationalizes prompt evaluation with transparent metrics and clear visualizations. The sales projection study shows how the framework guides prompt choice in a practical forecasting scenario. Next steps include automated prompt search, adversarial prompt detection, and human in the loop evaluation to complement automatic metrics.

References

[1] Brown et al., “Language Models are Few Shot Learners,” NeurIPS 2020. [2] Wei et al., “Chain of Thought Prompting Elicits Reasoning in LLMs,” arXiv:2201.11903. [3] Ouyang et al., “Training language models to follow instructions with human feedback,” arXiv:2203.02155. [4] Liu et al., “Pre train Prompt Tuning,” ACL 2023. (Framework elements and task/metric descriptions draw from your original paper draft. )

Copyright

Copyright © 2025 Princy Hareshbhai Patel, Sanjana Suthar, Prof. Hiren Mer. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.