The work is dedicated to the study of large language models (LLMs) and approaches to improving their efficiency in a new service. The rapid development of LLMs based on transformer architecture has opened up new possibilities in natural language processing and the automation of various tasks. However, fully utilizing the potential of these models requires a thorough approach and consideration of numerous factors.
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.