04016ntm a22005417i 4500 000714242 CZ-PrVSE 20240615111821.0 m d cr n|||||||||| 240615s2024 xr fsbm 000 0 eng d NEZPRACOVANÝ IMPORT ABA006 cze ABA006 ABA006 rda Caninec, Michal ISIS:154642 dis Velké jazykové modely (LLM) : Zhodnocení kvality generovaného textu pomocí dat specifických pro danou úlohu eng Large Language Models (LLMs) : Examining the quality of generated text with task specific data Michal Caninec 2024 ?? stran : digital, PDF soubor Vedoucí práce: Richard Antonín Novák Bakalářská práce (Bc.)—Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky, 2024 Obsahuje bibliografii Textový (vysokoškolská kvalifikační práce) Rok obhajoby 2024 Large Language Models (LLMs) have emerged as powerful tools for generating human-like text across various applications. This thesis examines the factors influencing the success of LLMs in text generation tasks, focusing on prompting techniques and the integration of external data within the prompt. The research aims to validate the role of prompt engineering in generating compelling content, particularly in the context of writing marketing newsletters. The thesis begins by introducing the research area of LLMs and prompting, followed by a literary review of relevant prompt engineering techniques. Based on the findings, a set of hypotheses is formulated, outlining the potential impact of these techniques on text generation quality. A controlled experiment is conducted to validate the hypotheses, applying the identified prompting techniques to the task of writing email newsletters with a single LLM (GPT-3.5-turbo-1106). The generated newsletters are evaluated using quantitative metrics and A/B/n test campaign to an audience (n=550) of registered attendees of a university event. The results of the experiment confirmed the important role of prompting techniques and external data. On the specific task of newsletter writing, usage of advanced prompting techniques resulted in 15% improvement in click-through rate for a given prompting technique leveraging external data (Improving the click-through rate from 4% to 19%), demonstrating prompt engineering and external data play an important role when creating effective texts that engage the audience. Techniques leveraging specification of tone of voice, providing additional context data, relevant examples or industry best practice instructions have shown to have the largest impact on the resulting text and achieved superior performance on the measured task compared to a base prompt solution without prompting techniques. Způsob přístupu: Internet aplikovaná informatika [obor bakal. práce] bakalářské práce fd132403 czenas bachelor's theses eczenas AI context data Generative AI model evaluation text generation LLM prompt engineering Novák, Richard, 1973- ntka173578 ths Máša, Petr ISIS:17194 opn Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky kn20010709399 dgg https://insis.vse.cz/zp/83508/podrobnosti VŠKP v InSIS https://insis.vse.cz/zp/83508 Hlavní práce https://insis.vse.cz/zp/83508/posudek/vedouci Hodnocení vedoucího https://insis.vse.cz/zp/83508/posudek/oponent/82175 Oponentura https://insis.vse.cz/zp/83508/podrobnosti dc:identifier NEPOSILAT VSKP vse83508 240612 83508