AI-Enhanced Q&A-Summary System Using Advanced Prompt Techniques
Avikal Chauhan, CH Pawan, C Vishwash, Aditya Dillon, Bharani Kumar Depuru · International Journal of Innovative Science and Research Technology (IJISRT) · 2025
The swift progress of large language models [1] in recent times has profoundly influenced the trajectory of natural language processing. This evolution has been propelled by exponential growth in computational resources, the increasing availability of expansive data, and refinements in algorithmic methodologies. Transitioning from the rudimentary rule- based frameworks to today’s complex architectures, LLMs have undergone substantial transformation. Early models showcased the capacity for generating coherent and contextually applicable content but recent enhancements have significantly augmented both comprehension and content generation capabilities marking a pivotal leap in language model sophistication. The expansion of open-source LLMs [2] has transformed the sphere of advanced linguistic innovations, contributing unprecedented access for experimentation and implementation across sectors, such as education recent breakthroughs in prompt enhancing have redefined how these models are harnessed, producing very accurate contextually aware outputs without requiring exhaustive retraining processes. Within the educational domain, large language models LLMs present a paradigm shift by streamlining text automation significantly mitigating the laborious, and resource-heavy demands of traditional manual tasks. This advancement empowers educators to devote more attention to pedagogy and direct student interaction. On top of that, the fusion of sophisticated LLMs accompanied by optimized prompting strategies [3] in scholastic platforms elevates the educational experience, delivering tailored high-caliber, and contextually pertinent material. This approach fosters a more adaptive and systematic learning ecosystem enhancing the overall instructional framework.