International Journal For Multidisciplinary Research

E-ISSN: 2582-2160     Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 8, Issue 3 (May-June 2026) Submit your research before last 3 days of June to publish your research paper in the issue of May-June.

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Author(s) Mr. Ankit Kumar, Mr. Sutiksh Kumar Srivastav, Mr. Amit Paswan, Mrs. Anjum Ahsan
Country India
Abstract The rapid expansion of digital platforms has created a significant demand for high-quality and timely content generation across various domains such as education, marketing, journalism, and social media. Traditional manual content creation is time-consuming, resource-intensive, and often inconsistent in quality.

To address these challenges, this paper presents an AI-powered content generation system that leverages advanced Natural Language Processing techniques and transformer-based deep learning models. The proposed system is designed to automatically generate coherent, context-aware, and human-like textual content based on user-provided inputs or prompts. Transformer architectures utilize self-attention mechanisms to effectively capture long-range dependencies and semantic relationships within text, resulting in improved fluency and relevance of generated content.

In addition, prompt optimization strategies are incorporated to guide the model toward producing more focused and meaningful outputs. The system is implemented using a pre-trained language model and evaluated through both automatic evaluation metrics, including BLEU and ROUGE scores, and qualitative human assessment. Experimental results demonstrate that the proposed approach significantly enhances content relevance, coherence, and readability when compared to baseline text generation methods.

The findings highlight the effectiveness of AI-driven content generation systems and their potential to reduce human effort while maintaining content quality. This research also discusses existing limitations such as bias and factual inaccuracies and outlines future directions for developing more reliable and ethical AI content generators.
Keywords Content Automation,Digital Marketing Content ,AI in Education ,Social Media Content Generation, Journalism Automation ,SEO Content Generation
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 8, Issue 3, May-June 2026
Published On 2026-05-05

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