Improving Large Language Model Responses Using Shot-Oriented Learning

Abstract

All shot-based learning techniques are very much useful for train the models just with a small number of datasets. The models are trained by large datasets always producing a better result but it is very challenging, tough or expensive task to collecting the large datasets. So, with the help of prior knowledge and small datasets, all these learning approaches providing benefits to different domains like Natural Language Processing (NLP), robotics and image recognitions. All these learning approaches faced a lot of challenges because of the complexity of their learning process with a small dataset and it leads to in accurate results and it is also toughest job to generalize unseen data. So, this research work aims to educate the end users with different query formats while their interaction with ChatGPT and also enhancing the model’s accuracy by adopting techniques like transformers and fine-tuning. Each shot-based approach provides different types of levels of accuracy that is totally depending upon user’s request, but N-shot learning approach always produced better result consistently when compared to other learning approaches. By utilizing well-structured query formats helps to the end users to get more effective and accurate responses from AI models like ChatGPT with N-shot learning approach.

Introduction

Shot-based learning approaches such as zero-shot, one-shot, few-shot, and N-shot learning enable AI models to learn and perform tasks using very limited labeled data, making them highly useful when collecting large datasets is difficult, expensive, or time-consuming. These methods rely on pre-trained models and prior knowledge to recognize new patterns from a small number of examples, and are widely applied in areas like natural language processing, image recognition, robotics, object detection, and recommendation systems. They help reduce training time and resource requirements while improving adaptability in real-world applications.

However, these approaches also face challenges such as lower prediction accuracy when data is extremely limited, difficulty in generalizing to unseen data, and complex training requirements. To address these issues, the paper proposes a user-guidance framework that helps users select appropriate query formats (zero-shot, one-shot, few-shot, or N-shot) when interacting with AI models like ChatGPT. The goal is to improve response quality and structure by matching the right prompting strategy to the task.

The methodology explains how different query formats influence model outputs based on examples provided, and highlights that N-shot learning generally produces the most accurate and reliable results compared to other shot-based approaches. The study also compares efficiency levels across methods and emphasizes that selecting the correct prompting strategy can significantly improve AI performance in practical applications.

Conclusion

The concluding section presented a newly recommended methodology that provides a standardized format for end users to engage more successfully with AI models such as ChatGPT. The end users can realize several learning methodologies, including zero-shot, one-shot, few-shot, and N-shot, they can customize their inquiries and attain more efficient, precise, and pertinent solutions from ChatGPT. This research work suggested a new methodology which guarantees that end users can receive structured outputs across several domains, including categorization, text production, and data analysis, contingent upon the syntax of their queries. The final findings suggest that the N-shot learning strategy consistently achieves the maximum accuracy, highlighting the significance of the query structure for effective and precise interactions with AI models such as ChatGPT.

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Copyright

Copyright © 2026 Dr. Manne Venu, Ramala Ashok, Prasanjit Singh, Praveen , Paindla Thirupathi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.