Prompt Engineering in Large Language Models: A Systematic Survey of Optimization Techniques and Real-World Applications

Abstract

Consequently, over the past decade, prompt engineering as a transformal technique has emerged to optimally facilitate large language models (LLMs) and vision language models (VLMs) for their transferable usage in various fields without the requirement of model retraining. The focus of this paper is on a thorough analysis of the optimization techniques available in prompt engineering field such as Zero Shot, Few Shot prompting, Chain of Thought, Auto CoT, Logical CoT (LogiCoT) and Retrieval augmented generation (RAG). These techniques boost the performance of LLMs on real-world use cases such as natural language understanding, commonsense reasoning and general solving sophisticated problems across domains in healthcare, finance, education and legal system.
Additionally, this paper studies the biases, fairnes, and hallucination problems in LLM outputs and thus highlights how optimized prompt strategies can resolve them and facilitate the model generalization. It also explains the importance of interpretability in AI systems and what current limitations are, as well as the need to use transparent prompt engineering approaches.
Some novel research directions suggested in the paper are meta learning for dynamic prompt adaptation, hybrid models that combine few of the techniques for optimized task performance, and the feasibility of an autonomous prompt engineering system. This research push forward to understanding these optimization techniques and in doing so, will help put forward more efficient and equitable LLM deployment across different applications. The conlcusion of the study is that putting the effort into prompt engineering will greatly push AI capabilities, while developing fair and interpretable systems.