Principled Instructions Are All You Need for Questioning LLaMA-1/2, GPT-3.5/4

Principled Instructions Are All You Need for Questioning LLaMA-1/2, GPT-3.5/4
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Original Paper
This paper introduces 26 guiding principles designed to streamline the process of querying and prompting large language models. Our goal is to simplify the underlying concepts of formulating questions for various scales of large language models, examining their abilities, and enhancing user comprehension on the behaviors of different scales of large language models when feeding into different prompts. Extensive experiments are conducted on LLaMA-1/2 (7B, 13B and 70B), GPT-3.5/4 to verify the effectiveness of the proposed principles on instructions and prompts design. We hope that this work provides a better guide for researchers working on the prompting of large language models. Project page is available at

Summary Notes

A Guide to Effective Communication with LLaMA and GPT Models for AI Engineers

The landscape of artificial intelligence (AI) is rapidly evolving, with Large Language Models (LLMs) like LLaMA and GPT series leading advancements in natural language processing (NLP).
These models, including LLaMA-1/2 and GPT-3.5/4, hold the promise of transforming our interaction with technology by automating complex tasks such as customer service and code generation.
To fully unlock their capabilities, AI engineers must master the art of communicating effectively with these models. This guide outlines key instructions and principles derived from extensive research and experimentation to improve the quality of responses from LLMs.

Understanding the Progress of Large Language Models

The development of LLMs, from Google's BERT to OpenAI's GPT-4, signifies a leap in NLP technology. These advancements have focused on creating models that are not only larger but smarter and more efficient, capable of generating human-like text with enhanced understanding. Prompting techniques like Ask-Me-Anything, Chain-of-Thought, and least-to-most prompting have played a crucial role in elevating the performance of LLMs, especially in answering complex questions.

Key Principles for Prompting LLMs

To facilitate effective interaction with LLMs, AI engineers should adhere to the following principles:
  • Clarity and Conciseness: Ensure your prompts are direct and to the point, avoiding overly polite or verbose language.
  • Audience Awareness: Craft your prompts with the intended audience or application in mind for more relevant outcomes.
  • Simplifying Complex Tasks: Break down complicated tasks into smaller, manageable prompts for better comprehension by the model.
  • Example-Driven Prompts: Use examples to steer the model toward the desired type of response.
  • Bias Avoidance: Be mindful to not include prompts that reinforce harmful biases or stereotypes.
  • Interactive Prompting: Engage in a dynamic interaction by allowing the model to ask follow-up questions for clarification.
  • Role Assignment and Delimiters: Utilize roles and delimiters in your prompts to maintain clear information flow and manage expectations.

Research Findings

Through the application of these principles across various LLM scales, including the ATLAS benchmark, we observed:
  • Notable enhancements in the responses' quality and accuracy from the LLMs.
  • Larger models exhibited more significant improvements, showcasing the scalability of these principles.

Looking Ahead

This investigation into principled questioning techniques for LLaMA and GPT models highlights a roadmap for better interactions with LLMs. Future avenues include refining models to align more closely with these principles through fine-tuning and reinforcement learning techniques.

Considerations and Future Directions

Although our findings are promising, they come with the caveat that the applicability of these principles can vary based on question complexity and the specific LLMs in use. Expanding our research to a broader range of models and questions is essential for validating these principles universally.


Principled instructions present a systematic approach for AI engineers to enhance interactions with LLMs, unlocking new levels of efficiency and innovation. As AI continues to advance, adapting and refining these principles will be key to harnessing the full potential of LLM technologies in various applications.

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