Prompt Engineering: Concepts, Frameworks, and Best Practices

With the rapid development of AI, prompts are essential for guiding large language models (LLMs) to produce desired outputs.

A prompt is a short text input that instructs the model; high‑quality prompts improve answer quality, efficiency, and reduce misunderstandings.

Several prompt‑engineering frameworks are introduced:

ICIO (Instruction, Context, Input, Output) – example for Java code refactoring.

BROKE (Background, Role, Objective, Key result, Evolve) – OKR‑style prompt design.

CRISPE (Capacity & Role, Insight, Statement, Personality, Experiment) – structured prompt composition.

TRACE (Task, Request, Action, Context, Example) – step‑by‑step task description.

Additional frameworks such as COT (Chain‑of‑Thought) and ToT (Tree‑of‑Thought) for reasoning.

Key principles for writing effective prompts include clear objectives, specific guidance, concise language, appropriate examples, and iterative optimization.

Practical prompt templates are provided for various scenarios: knowledge acquisition, cognitive testing, idea generation, code review, product ideation, title generation, and reverse questioning.

Common troubleshooting patterns for LLM responses (off‑topic, inaccurate, incomplete, verbose, multiple answers, overly conservative) and mitigation strategies are listed.

Resources for further learning are compiled, including websites (learningprompt.wiki, flowgpt.com, aishort.top, clickprompt.org, promptperfect.jinaai.cn) and open‑source repositories (Prompt‑Engineering‑Guide, Awesome‑Prompt‑Engineering, etc.).

Example code snippet for a Java refactoring prompt:

我是一位Java开发人员，需要对一段Java代码进行重构，以提高代码质量和可维护性。
你是一位Java开发专家，您将提供重构建议和解释。
对给定的Java代码进行重构，遵循SOLID、DRY等原则，并使用JavaDoc中文注释。

The article concludes with a brief team introduction and a list of reference links.