Comparative Study and Analysis of Prompting Techniques Using Gemini API Model and Reasoning Frameworks

Natural Language Processing (NLP) systems rely heavily on prompt engineering to enhance performance. This study evaluates various prompt techniques across different sizes of the Gemini API models (‘small’, ‘medium’, and ‘large’) using reasoning paradigms such as zero-shot, few-shot, Chain of Thought (CoT), and Tree of Thought (ToT). Metrics including accuracy, coherence, and latency were analyzed for prompts emphasizing specificity, brevity, example-driven accuracy, and creativity. Results indicate that the selection of reasoning paradigms and model sizes significantly impacts the performance, highlighting trade-offs between computational efficiency and quality. Recommendations for optimal configurations tailored to specific tasks are provided.