Prompting Techniques for Enhancing the Use of Large Language Models
The work is dedicated to the study of fundamental prompting techniques to improve the efficiency of using large language models (LLMs). Significant attention is given to the issue of prompt engineering. Various techniques are examined in detail: zero-shot prompting, feedback prompting, few-shot prompting, chain-of-thought, tree of thoughts, and instruction tuning. Special emphasis is placed on Reaction & Act Prompting and Retrieval Augmented Generation (RAG) as critical factors in ensuring effective interaction with LLMs.