This report outlines the research focused on finding the system design for Happymaker AI, a large language model with a mission to promote well-being at workplaces through daily interactions. The study includes a market analysis of relevant system components, such as database, cloud storage, cloud computing service and large language model, as well as the development of a prototype. Despite facing challenges including limited training data and resource constraints, the prototype was developed using the Llama 2 13B model which was quantized to 8-bits and fine-tuned using LoRA. Through research and prototyping of Happymaker AI, recommendations for the system design were established. These findings provide a foundation for the further development of an ethical AI system, specifically tailored for user data security and scalability. The findings also introduce a new perspective on empathy and personal well-being within the AI field, emphasizing the importance of integrating human-centric values into technological advancements. / Denna rapport skildrar forskningen som fokuserade på att hitta systemdesignen för Happymaker AI, en stor språkmodell med uppdraget att främja välmående på arbetsplatser genom dagliga interaktioner. Studien inkluderar en marknadsanalys av relevanta systemkomponenter såsom databas, molnlagring, molntjänster och en stor språkmodell, samt utvecklingen av en prototyp. Trots utmaningar, inklusive begränsad träningsdata och resursbegränsningar utvecklades prototypen med modellen Llama 2 13B som kvantiserades till 8-bit och tränades med LoRA. Genom forskning och prototypframtagning av Happymaker AI fastställdes rekommendationer för systemdesignen. Resultaten av studien ger en grund för vidareutveckling av ett etiskt AI-system som är anpassat för användardatasäkerhet och skalbarhet. Samtidigt introduceras ett nytt perspektiv på empati och personligt välmående inom AI-fältet, vilket betonar vikten av att integrera människocentrerade värderingar i teknologiska framsteg.

In this research project, we aim to explore the use of Large Language Models (LLMs) as an interface to interact with API tools in natural language. Bubeck et al. [1] shed some light on how LLMs could be used to interact with API tools. Since then, new versions of LLMs have been launched and the question of how reliable a LLM can be in this task remains unanswered. The main goal of our thesis is to investigate the designs of the available system prompts for LLMs, identify the best-performing prompts, and evaluate the reliability of different LLMs when using the best-identified prompts. We will employ a multiple-stage controlled experiment: A literature review where we reveal the available system prompts used in the scientific community and open-source projects; then, using F1-score as a metric we will analyse the precision and recall of the system prompts aiming to select the best-performing system prompts in interacting with API tools; and in a latter stage, we compare a selection of LLMs with the best-performing prompts identified earlier. From these experiences, we realize that AI-generated system prompts perform better than the current prompts used in open-source and literature with GPT-4, zero-shot prompts have better performance in this specific task with GPT-4 and that a good system prompt in one model does not generalize well into other models.

Identifying the most used design patterns and communication methods for a microservices architecture holds paramount importance in ensuring system stability within the IT industry. With a plethora of approaches available, encompassing various design patterns and communication techniques, the selection of technologies often hinges upon the expertise and knowledge of software designers. However, designing microservices solely through human efforts poses significant challenges for companies. Moreover, the monitoring aspect presents its own set of complexities in the realm of microservices. This research aims to elucidate the prevalent design patterns and communication methods utilized in modern IT microservices environments. It also seeks to demonstrate the integration of Artificial Intelligence tools such as ChatGPT into the architecture design process, particularly in enhancing monitoring capabilities within microservices. Subsequently, a prototype is proposed to offer design guidance, as recommended by the ChatGPT and Llama 2 customized models in accordance with the specified requirements. Additionally, the study primarily seeks to identify prevalent design patterns and communication methods employed within the IT industry through the utilization of surveys and interviews conducted with both technical and non-technical personnel.

I en tid då AI-språkmodeller används alltmer i vår vardag, blir det relevant att undersöka hur det påverkar samhället. Denna studie undersöker, utifrån teorier om etik och jämställdhet, hur AI-språkmodeller i sina texter ger uttryck för mångfald, icke-diskriminering och rättvisa. Studien fokuserar på att identifiera och analysera förekomsten av könsbias i AI-språkmodellernas svar samt hur det påverkar människors förtroende för dessa system. En fallstudie genomfördes på tre AI-språkmodeller - ChatGPT 3.5, Gemini och Llama-2 70B, där data insamlades via intervjuer med dessa modeller. Därefter gjordes intervjuer med mänskliga informanter som reflekterade över AI-språkmodellernas svar. AI-språkmodellerna visade en obalans i hur de behandlar kvinnor och män vilket kan förstärka befintliga könsstereotyper. Detta kan påverka människors förtroende för AI-språkmodeller och informanterna lyfte problematiken om vad neutralitet och rättvisa innebär. För att skapa mer ansvarsfulla och rättvisa AI-system krävs medvetna insatser för att integrera etiska och jämställdhetsperspektiv i AI-utveckling och användning. / In a time when Large Language Models (LLMs) are increasingly used in our daily lives, it becomes important to investigate how this affects society. This study examines how LLMs express diversity, non-discrimination, and fairness in texts, based on theories of ethics and gender equality. The study focuses on identifying and analyzing the presence of gender bias in the responses of LLMs and how this impacts people's trust in these systems. A case study was conducted on three LLMs: ChatGPT 3.5, Gemini, and Llama-2 70B, where data was collected through interviews with them. Subsequently, interviews were conducted with human informants who reflected on the LLMs’ responses. The LLMs showed imbalance towards gender, potentially reinforcing existing gender stereotypes. This can affect people's trust in LLMs, and the informants highlighted the issue of what neutrality and fairness entail. To create more responsible and fair AI systems, conscious efforts are required to integrate ethical and equality perspectives into AI development and usage.