Prompt Engineering and Resource Efficiency: An Empirical Study on the Sustainable Use of Large Language Models

This study explores reducing the computational resource consumption of large language models (LLMs) through optimizing prompt design and user interaction patterns, using a systematic analysis framework that combines real datasets and controlled experiments. Core research questions include: the impact of prompt structure on token consumption and response length, efficiency differences across task types, and the feasibility of efficiency modeling. The study aims to provide a quantitative analysis framework and practical recommendations for the sustainable use of LLMs.

LLMs are widely used in daily work and creation, but the issue of resource consumption is becoming increasingly prominent, with significant differences in usage efficiency among different users (precise prompts vs. multi-round inefficient interactions). The core question of this study: Can we significantly reduce LLM resource consumption through optimizing prompt design and user behavior?

Two types of data are used: 1. Open dialogue datasets (e.g., real user dialogues in the style of ShareGPT); 2. Synthetic prompt experimental data (with controlled variables to isolate the impact of specific factors). Data processing is divided into three levels: raw files (01_raw), cleaned and filtered dialogues (02_processed), and dialogue-level feature tables (03_features), ensuring reproducibility and traceability.

Multi-dimensional analysis methods: 1. Exploratory data analysis (statistical prompt length, token patterns, novelty embedding to identify innovative/repetitive patterns); 2. Topic modeling (Sentence Transformer embedding + CountVectorizer feature extraction + UMAP dimensionality reduction + BERTopic clustering to analyze resource efficiency of each topic); 3. Efficiency prediction modeling (define target success rate and target cost indicators, build regression models using scikit-learn, analyze feature contributions via SHAP values and permutation importance); 4. Controlled experiments (compare efficiency differences between different prompt variants and the trade-off between efficiency and quality).

Technical tools include: data processing (pandas, numpy), machine learning (scikit-learn), visualization (matplotlib, seaborn), text embedding (sentence-transformers), token calculation (tiktoken).

Expected Contributions: Provide researchers with a reproducible LLM efficiency analysis framework; help practitioners identify inefficient patterns and give prompt optimization recommendations; touch on AI sustainability issues—optimizing interaction efficiency can produce significant cumulative effects.

Limitations: Use token count and interaction rounds as proxy indicators for resource consumption, without directly measuring energy consumption (actual energy consumption is also affected by model architecture, hardware, etc.).

Future Directions: Explore the connection with real energy consumption data; expand efficiency analysis to multi-modal scenarios; systematically compare efficiency characteristics of prompt strategies such as chain-of-thought and few-shot learning.