International Journal of Scientific Research and Engineering Development

A key technique for getting dependable and superior behavior out of Large Language Models (LLMs) is prompt engineering. The evolution of prompt engineering techniques from basic zero-shot and few-shot prompts to sophisticated reasoning frameworks like Chain-of-Thought (CoT) and Tree-of-Thought (ToT) is thoroughly reviewed in this paper. We divide methods into four categories: task-specific strategies, optimization and automated prompting, retrieval and knowledge-enhanced prompting, and reasoning-oriented prompting. We analyze applications across text classification, question answering, mathematical problem solving, commonsense reasoning, and code generation. Finally, we discuss challenges including hallucination, bias, scalability, and interpretability, and outline promising future directions such as meta-prompting.