[Paper Review] Prompt Design and Engineering: Introduction and Advanced Methods

Xavier Amatriain|arXiv (Cornell University)|Jan 24, 2024

BIM and Construction Integration31 citations

TL;DR

This paper surveys prompt design and engineering for LLMs, introducing core concepts and advanced techniques such as Chain-of-Thought, Reflection, and agent-based prompting, plus a survey of tooling.

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.

Motivation & Objective

• Define prompt design and engineering for generative AI models.
• Explain core prompt types and how they influence model outputs.
• Survey advanced techniques (CoT, Reflection, ToT, tools, Rails, RAG) and their applications.
• Discuss limitations of LLMs and how advanced prompting mitigates them.
• Provide a landscape of tools and frameworks used by prompt engineers.

Proposed method

• Describe basic and advanced prompt structures and prompt engineering workflows.
• Introduce Chain-of-Thought and other reasoning-enhancement techniques with examples.
• Present advanced methods including ToT, Tools/Connectors/Skills, ART, Self-Consistency, Reflection, Expert Prompting, and Rails.
• Discuss automation and tooling such as APE and PromptChainer-based chains.
• Outline Retrieval Augmented Generation (RAG) and related prompting strategies.
• Reference notable related works to contextualize methods.

Figure 1: Instructions + Question Prompt result example

Experimental results

Research questions

• RQ1 What are the foundational concepts of prompt design and engineering for LLMs?
• RQ2 Which advanced prompting techniques improve reasoning, factuality, and reliability of LLM outputs?
• RQ3 How can prompts be engineered to leverage external tools, memory, and knowledge sources?
• RQ4 What tooling architectures (agents, Rails, APE) support scalable prompt engineering?
• RQ5 What are the core limitations of LLMs that prompting strategies aim to mitigate?

Key findings

• Chain-of-Thought prompting and its variants improve problem solving in LLMs.
• Techniques like Tree of Thought and self-consistency enhance exploration and reliability.
• External tools, connectors, and skills extend LLM capabilities beyond intrinsic knowledge.
• Reflection, expert prompting, and rails guide outputs toward accuracy and safety.
• Automatic prompt engineering and RAG approaches automate and augment prompt design.
• Prominent frameworks and tools (e.g., PromptChainer, Nemo Guardrails, APE) illustrate practical engineering workflows.

Figure 2: Instructions + Input Prompt result example