Prompt Tips: Structured Instructions Improve LLM Task Decomposition Ability

When interacting with a large language model (LLM), how to make the model understand complex tasks more efficiently and give high-quality output is one of the core challenges of Prompt Engineering. Recently, a technique called "structured instructions" has attracted attention in the community. This method significantly improves the performance of LLM in multi-step reasoning and complex problem solving by breaking down tasks into clear steps and giving the model clear roles. This brief will explore the principles and applications of this technique in depth to help you quickly get started in real-world scenarios.

Tip 1: Structured instructions to break down complex tasks

Background and Discovery

A Twitter influencer pointed out that clear prompts are the key to improving the quality of LLM output, and suggested optimizing the interaction effect through specific instructions. At the same time, the r/PromptEngineering community on Reddit discussed on March 6 how to use structured instructions to improve LLM's task decomposition capabilities. This method originated from the evolution of Chain-of-Thought (CoT), but it emphasizes the modularity and role definition of instructions, and is suitable for scenarios that require step-by-step reasoning or multi-task collaboration.

Core Principles

The core of structured instructions is to split complex tasks into multiple independent and executable sub-steps and specify clear roles for the model (such as "analyst" and "planner"). This not only reduces the cognitive burden of the task, but also reduces the ambiguous output of the model through clear contextual constraints. Studies have shown that LLMs tend to follow structured guidance rather than vague single instructions when dealing with multi-level problems.

Implementation Methods

1. Define roles : Assign a specific role to the model and clarify its task perspective.

2. Split the task : Break down complex problems into 3-5 clear sub-steps.

3. Provide context : Provide necessary background information for each step.

4. Set output format : Explicitly ask the model to answer in steps and specify the format (such as list, table).

5. Iterative verification : Adjust instructions based on initial output to ensure logical consistency.

Prompt Example

1You are a professional task planner. Your goal is to help me develop a 3-day study plan for the topic "Mastering Python Basics". Please answer as follows:

21. Analysis goal: clarify the core content of "Mastering Python Basics".

32. Allocate time: Allocate these contents reasonably to 3 days, and the study time per day should not exceed 4 hours.

43. Output plan: List the specific tasks and time schedule for each day in a table.

5Make sure each step is clear and logical.

Applicable scenarios

• Multi-step reasoning : such as solving math problems and developing project plans.
• Requires structured output : such as generating reports and designing processes.
• Education and training : Help learners break down complex knowledge points.

Effect comparison

Normal Prompt :

Help me make a 3-day plan to learn Python basics.

The output may be a general paragraph that lacks specificity and organization.

Structured Prompt (as shown in the example above):
The output is a clear table that breaks down the goals into steps, with clear daily tasks, and the learning efficiency is improved by about 30% (based on community feedback).

Summarize

Structured instructions make LLM more stable and efficient in complex scenarios through role definition and task decomposition. Whether it is making plans or solving multi-step problems, this technique can significantly improve the output quality. It is recommended that you try to split the task into clear steps and clarify the role of the model the next time you use LLM. The effect is often better than expected. In the future, this method can be further strengthened in combination with automatic optimization tools (such as Prompt Optimizer).