The Open AI language model is a powerful tool for generating AI content. A Large amount of text data is trained through the language model, which can generate new text that is similar in style and tone to the training data. This language model can assist writers in generating high-quality content by offering suggestions and insights for improving language usage, sentence structure, and overall readability. This study represents the development of a content generation tool based on the open AI language model by utilising GPT 3 in the backend as an API to generate the necessary information for the model. With the help of this tool, businesses and individuals can produce high-quality, engaging content more efficiently than ever before. This content generation tool uses a recurrent neural network (RNN) architecture, which enables it to make more accurate predictions than rule-based chatbots. All the features, like Facebook ads, LinkedIn posts, Amazon product descriptions, blogs, company bios, chat bots, and so on, will be presented in the dashboard. This tool is powered by advanced machine learning algorithms that can analyse and understand natural language, allowing them to produce content that is grammatically correct, free of errors, and tailored to specific audiences. They can also help optimize content for search engines, ensuring that it reaches a wider audience and generates more traffic with fine-tuning templates.

This research presents a comprehensive framework for building customized chatbots empowered by large language models (LLMs) to summarize documents and answer user questions. Leveraging technologies such as OpenAI, LangChain, and Streamlit, the framework enables users to combat information overload by efficiently extracting insights from lengthy documents. This study discussed the framework's architecture, implementation, and practical applications, emphasizing its role in enhancing productivity and facilitating information retrieval. Through a step-by-step guide, this research has demonstrated how developers can utilize the framework to create end-to-end document summarization and question-answering applications.

The Retrieval-Augmented Generation (RAG) model significantly enhances the capabilities of large language models (LLMs) by integrating information retrieval with text generation, which is particularly relevant for applications requiring context-aware responses based on dynamic data sources. This research study presents a practical implementation of a RAG model personalized for a Chabot that answers user inquiries from various specific websites. The methodology encompasses several key steps: web scraping using BeautifulSoup to extract relevant content, text processing to segment this content into manageable chunks, and vectorization to create embeddings for efficient semantic search. By employing a semantic search approach, the system retrieves the most relevant document segments based on user queries. The OpenAI API is then utilized to generate contextually appropriate responses from the retrieved information. Key results highlight the system's effectiveness in providing accurate and relevant answers, with evaluation metrics centered on response quality, retrieval efficiency, and user satisfaction. This research contributes a comprehensive integration of scraping, vectorization, and semantic search technologies into a cohesive chatbot application, offering valuable insights into the practical implementation of RAG models.

The rapid acceleration in the development of AI-in particular, generative AI-is changing the nature of work globally, with great significant for job creation, transformation, and displacement. Drawing on major studies, employer forecasts, and AI-driven evaluations, this review paper synthesizes evidence to investigate sectoral vulnerabilities to automation across different economic contexts. In doing so, it identifies professions and sectors that have emerged as particularly vulnerable to disruption by AI, with a specific focus on developments relating to generative AI since 2023. Clerical, administrative, financial, and customer service jobs are currently identified as those globally at the highest risk, while knowledge-based and creative jobs that have been considered hitherto safe are increasingly vulnerable. Conversely, occupations that are physically and emotionally demanding and unpredictable, such as health care, skilled trades, and hospitality, remain comparatively resilient. This review also explores regional variation in risks from automation, approaches to the methodological assessment of risk, and the strategic responses from employers across industries. Conclusively, this study emphasizes a set of policy recommendations targeting concerted upskilling, AI governance, and inclusive transition strategies in efforts to prevent labor markets from becoming more unequal. This systematic literature review used information obtained from peer-reviewed journals, policy reports, and organizational datasets published between 2013 and 2025. Altogether, 52 studies were thematically analyzed and comparatively mapped across sectors in line with predetermined inclusion criteria targeted at AI-driven automation and workforce vulnerability across sectors.

Analyzing patient feedback on drug reviews is crucial in the healthcare sector as it determines the efficacy of treatment and patient experiences. Amidst the exponential growth in patient-generated data, the method of sentiment analysis has emerged as a key means of interpreting text-based reviews. In this research, the use of various machine learning and transformer-based approaches to analyze sentiments in drug reviews and gain meaningful insights from patient reviews or opinions is outlined. It juxtaposes traditional machine learning models such as Logistic Regression, Random Forest, and Support Vector Machines with deep neural networks such as Long Short-Term Memory and transformer-based models such as Bidirectional Encoder Representations from Transformers (BERT). Various models' performance is tested using the UC Irvine drug review dataset, and data preprocessing, feature extraction, and cross-validation are used in the study. Transformers, more precisely BERT, perform better than conventional approaches at 0.96 accuracy based on findings, as they can read into intricate patterns of language and contextual hints undetectable by basic models. The research reveals how transformer-based sentiment analysis can enhance healthcare decision-making through better and context-based information. Received: 30 September 2024 | Revised: 14 January 2025 | Accepted: 4 September 2025 Conflicts of Interest The author declares that he has no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in UC Irvine Machine Learning Repository at https://archive.ics.uci.edu/dataset/461/drug+review+dataset+druglib+com. The data that support the findings of this study are openly available in GitHub at https://github.com/imabhi01/sentiment-analysis-dissertation. Author Contribution Statement Abhishek Chaudhary: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing—original draft, Writing—review & editing, Visualization, Project administration. Sangita Pokhrel: Supervision, Writing—review & editing. Swathi Ganesan: Writing—review & editing. Prashant Bikram Shah: Investigation, Writing—review & editing. Nalinda Somasiri: Writing—review & editing.

The rapid advancement of generative artificial intelligence (AI) has transformed digital marketing, redefining how brands create, personalize, and deliver content to engage consumers. As organizations increasingly rely on AI-driven systems for customer interaction, understanding how these technologies influence customer engagement has become a critical area of inquiry. This study systematically reviews research published between 2022 and 2025 to evaluate the impact of generative AI on customer engagement, distinguishing between affective outcomes (e.g., satisfaction, trust, and commitment) and behavioral outcomes (e.g., click-throughs, shares, purchases, and retention). Following PRISMA guidelines, a comprehensive search across major databases (Scopus, Web of Science, and Google Scholar) identified 528 records, of which 64 articles were assessed for eligibility, and 33 studies met the final inclusion criteria for qualitative synthesis. Findings indicate that generative AI tools, particularly large language models (LLMs) for conversational marketing and generative adversarial or diffusion models for visual content, generally associated with positive behavioral engagement outcomes, such as improved attention, interactivity, and conversion rates, although these effects vary depending on context, platform, and implementation. However, affective engagement outcomes remain mixed; while personalization and novelty foster satisfaction and delight, authenticity concerns often hinder trust and emotional connection. Two moderating factors, AI content disclosure and human-in-the-loop (HITL) oversight, emerged as critical influences. Transparent disclosure can enhance credibility in some contexts but evoke skepticism in others, while human oversight consistently reinforces brand authenticity, ethical quality, and consumer confidence. Practical implications include guidance on effective AI disclosure strategies, when and how to be transparent about AI generation and the importance of maintaining a human touch in AI-augmented marketing. The review also identifies key research gaps, including long-term effects on brand loyalty, cross-cultural differences, and ethical and legal implications, and proposes a future research agenda to advance knowledge in this rapidly evolving field.