A comprehensive taxonomy for large language models

Sub-headline:RUC researchers and collaborators systematize prompt engineering techniques,building a multi-dimensional framework for generative AI optimization.

A significant technical pain point in the LLM era is the high dependency of output quality on input instructions,yet prompt engineering lacks a unified theoretical structure.Most current techniques are scattered across empirical studies,leaving developers in a"trial-and-error"loop without methodological guidance.This lack of systematic support is particularly problematic in professional fields where stability and reasoning depth are required,hindering the effective deployment of large-scale generative models in high-stakes environments.

In response to these challenges,the research team from Renmin University,in collaboration with Tsinghua and Microsoft,developed a comprehensive taxonomy of prompt engineering.This innovation treats prompting as a structured engineering paradigm rather than a collection of tricks.The framework deconstructs the field into six levels:fundamental construction,in-context learning with examples,reasoning-based prompting,interactive agent-based prompts,and safety-oriented strategies.This design allows complex instruction tuning to be traceable and replicable across different model architectures.

Research indicates that systematic prompt strategies effectively bridge the gap between pre-training objectives and downstream application needs.Data analysis suggests that techniques like Chain-of-Thought(CoT)and adaptive feedback significantly boost accuracy in mathematical and programming tasks.This work provides a reliable technical roadmap for researchers and practical guidelines for developers,offering a robust foundation for building intelligent,cost-effective systems that maximize the synergy between human intent and machine intelligence.