Prompt Engineering: Unlocking the Potential of Large Language Models
Avinash N Bhute*
Associate Professor, Department of Computer Engineering, Pimpri Chinchwad College of Engineering, Nigdi, Pune, Maharashtra State, India
*Corresponding Author: Avinash N Bhute, Associate Professor, Department of Computer Engineering, Pimpri Chinchwad College of Engineering, Nigdi, Pune, Maharashtra State, India.
Published: July 12, 2024
Abstract
Large language models (LLMs) are revolutionizing the way we interact with information. But these powerful tools do not operate in a vacuum. We need instructions to guide LLMs towards the desired outcome. This is where prompt engineering comes in. LLMs are trained on massive datasets of text and code. While this imbues them with vast knowledge, it doesn't guarantee they will understand our specific requests. A poorly written request can send the LLM into a rabbit hole, resulting in meaningless or irrelevant text.
Prompt engineering is a technique in artificial intelligence (AI) that optimizes and fine-tunes language models for specific activities and intended outcomes. Also known as prompt design, it is the act of carefully creating prompts or inputs for AI models in order to improve their performance on specific tasks. Prompts are used to instruct and fine-tune the AI system's desired behaviour, as well as to acquire accurate and desirable responses from AI models. Generative AI systems, powered by transformer architectures, are designed to produce specific outputs based on the quality of provided prompts. Prompt engineering ensures that these AI models comprehend and respond effectively to a wide range of queries, from simple to highly technical. The fundamental rule is simple: good prompts yield good results.
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