Prompt Engineering Techniques in Large Language Models: A Study
Keywords:
Artificial Intelligence, Chain-of-thought, Large language models, NLP, Prompt engineering, Zero-shot learningAbstract
Large Language Models (LLMs) are revolutionizing the field of Artificial Intelligence by empowering computers to produce text that is comparable to natural language, make logical decisions, write coding scripts, and help make complex decisions. The effectiveness of these models is largely dependent on the structure, clarity, and context of the prompts given to them. As a result, prompt engineering has evolved as a powerful paradigm to effectively direct LLMs towards producing accurate, informative, and context-driven text. This study undertakes a detailed exploration of prominent prompt engineering techniques, namely Zero-Shot Prompting, Few-Shot Prompting, Chain of Thought Prompting, Role-Based Prompting, Instruction Tuning, Self-Consistency, and Prompt Chaining. The study undertakes a detailed analysis of the underlying principles of each of these techniques, their pros and cons, as well as their suitability for various domains of application. A comparative evaluation is carried out to assess the trade-offs of each of these techniques based on their reasoning power, computation costs, accuracy of responses, and implementation complexities. Additionally, challenges such as prompt sensitivity, hallucinations, and bias are discussed to further emphasize the importance of reliability and ethics within LLMs. The paper further explores new research avenues that are being pursued within the field of LLMs, including prompt optimization through automation and multimodal AI systems. The study shows that structured and strategic prompt engineering is essential for improved reasoning capabilities of LLMs, further reinforcing the importance of this component within AI systems.
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