Prompt Design and Engineering: Introduction and Advanced Methods

TL;DR

The paper investigates how prompt design can maximize LLM performance and reliability. It surveys basic prompt types, LLM limitations, and a wide range of advanced methods, including chain-of-thought, tree-of-thought, reflection, self-consistency, Rails, and RAG, as well as the role of LLM agents. It reviews supporting tools and frameworks (e.g., LangChain, Semantic Kernel, Nemo Guardrails) and discusses how these techniques enable scalable reasoning, external knowledge integration, and autonomous task execution. Overall, the work argues that sophisticated prompt engineering will be essential for building robust, scalable AI systems that combine internal reasoning with external data and automated workflows.

Abstract

Prompt design and engineering has rapidly become essential for maximizing the potential of large language models. In this paper, we introduce core concepts, advanced techniques like Chain-of-Thought and Reflection, and the principles behind building LLM-based agents. Finally, we provide a survey of tools for prompt engineers.

Figures (27)

• Figure 1: Instructions + Question Prompt result example
• Figure 2: Instructions + Input Prompt result example
• Figure 3: Question + Examples Prompt results example
• Figure 4: Chain of thought prompting example
• Figure 5: Chain of thought prompting example