Prompt optimization for large language models

DOI: 10.31673/2412-9070.2025.050843

Abstract

The rapid development of large language models (LLMs) has significantly transformed natural language processing, enabling more natural and effective human-computer interaction. Large language models, such as GPT- BERT, and others, have demonstrated remarkable success in various natural language processing tasks, including text generation, translation, sentiment analysis, and more. However, the quality of the output from these models largely depends on how the input prompts are formulated. This paper explores prompt optimization strategies, including prompt engineering, automatic prompt tuning, and task-specific adaptation.
Prompt engineering is a crucial aspect for achieving high-quality results from large language models. It involves designing and testing various prompt formulations to find the most effective ones for specific tasks. For instance, in text generation tasks, it is essential to formulate the prompt correctly so that the model can understand the context and provide an appropriate response. Automatic prompt tuning is another important approach that allows for the automatic adjustment of prompts to achieve optimal results. This method uses machine learning algorithms to analyze and optimize prom-pts based on previous outcomes.
Task-specific adaptation is a vital aspect of prompt optimization. Large language models can be fine-tuned to perform various tasks, such as translation, sentiment analysis, text classification, and more. For each of these tasks, it is necessary to develop specific prompts that consider the task's nuances and ensure high-quality results. For example, in translation tasks, it is important to consider the context and cultural nuances of the language to ensure accurate translation.
We analyze existing studies, propose a categorization of optimization techniques, and present insights from practical experiments. In this study, we conducted a series of experiments using various language models and prompts to evaluate their effectiveness. The results showed that optimized prompts signifycantly improve the efficiency, predictability, and interpretability of language model responses. For instance, in text generation tasks, optimized prompts led to higher quality text, reduced grammatical errors, and improved logical structure. 
An important aspect of prompt optimization is considering the context and specifics of the task. This requires a detailed analysis of the task and the development of specific prompts that account for all aspects of the task. For example, in sentiment analysis tasks, it is important to consider the context and emotional state of the text to ensure accurate sentiment analysis. In text classification tasks, it is crucial to consider the specifics of the text and develop prompts that allow the model to accurately classify the text.
The findings indicate that optimized prompts significantly enhance the efficiency, predictability, and interpretability of language model responses. This allows for the use of large language models to solve various natural language processing tasks with high-quality results. For example, in text generation tasks, optimized prompts lead to higher quality text, reduced grammatical errors, and improved logical structure. In translation tasks, optimized prompts ensure accurate translation, considering the context and cultural nuances of the language.
Thus, prompt optimization is a crucial aspect for achieving high-quality results from large language models. Prompt engineering, automatic prompt tuning, and task-specific adaptation signifycantly improve the efficiency, predictability, and interpretability of language model responses. The analysis of existing studies and the results of empirical experiments confirm the importance of prompt optimization for achieving high-quality results from large language models.

Keywords: language model; natural language processing; query tuning; optimization; instructional design; GPT; zero-shot; few-shot; text generation; testing; algorithm; machine learning; query; mathematical model.