From “Hello World” to “AI Symphony”: The Advanced Path of Prompt Cascade

Mastering Claude’s 5-layer Prompt system is like learning a new programming language. Start with simple “Hello World” prompts and gradually upgrade to composing complex “AI symphonies”. Let’s explore these five layers of magic:

1. Core Incantation: User Requirement

This is the most fundamental and critical layer. A good User Requirement should clearly define the task you want AI to complete, including expected outcomes and any constraints.For example, instead of saying “Write an article”, you should say:Compose a 2,000-word tech trend analysis article focusing on AI applications in healthcare. Include the following:

• Three real-world AI application cases in medical diagnosis
• A brief technical principle for each case
• Quantitative data on how these applications improve medical outcomes
• Potential ethical issues and solutions
• Predictions of breakthrough advancements in the next 5 years Such detailed requirements enable AI to generate more structured and information-rich content.

2. Fundamental Mantra: System Prompt

This is the official behind-the-scenes system prompt tailored for the model. Although we cannot modify it directly, understanding its existence helps us design our own prompts better.For example, Anthropic’s publicly released [3] Claude system prompt explicitly instructs Claude to maintain intellectual humility, admit possible errors, and encourage users to think independently. This means when designing prompts, we can focus more on the task itself rather than repeatedly emphasizing basic principles.

3. Magic (Magic Barrier): Global Rule

Project: Electric Vehicle Market Analysis

• Background: Analyze global EV market trends from 2024 to 2028
• Instructions:
• Output Format: All analyses should include data support, use charts to visualize key trends, and provide actionable insights.

4. Rune Combination: Task Decomposition

Breaking down complex tasks into executable subtasks is key to improving AI output quality. For example, when analyzing the EV market, decompose it into:

• Collecting industry data from 2023 onwards (e.g., sales volume, technical patent counts)
• Sorting out the technical roadmaps of the top 5 manufacturers (e.g., Tesla’s 4680 battery, BYD’s Blade Battery)
• Policy analysis: Subsidy phase-outs and carbon emission standards in various countries (e.g., EU’s 2035 fuel vehicle ban policy)
• Consumer survey data: Dimensions such as range anxiety and charging facility satisfaction

5. Ultimate Incantation: Feedback Loop

Establish an iterative optimization mechanism to enhance output quality through multi-round interactions. For example:

1. After the first output, check data timeliness (e.g., whether it includes Q1 2024 latest sales)
2. Verify the depth of technical analysis (e.g., whether the solid-state battery mass production timeline cites manufacturer financial reports)
3. Supplement missing dimensions based on feedback (e.g., omitted policies in emerging markets)
4. Before the final output, ask AI to summarize core conclusions in 3 bullet points

Practical Skills for Mastering the 5-Layer System

• Style Switching Guide:For example, you can create the following styles: By flexibly switching between these styles, you can make AI outputs perfectly adapt to different work scenarios.