What is the difference between AI prompt words and prompt word engineering, and case demonstration

1. What is a prompt word?

The English explanation of the prompt word is: Prompt.

A prompt is a question, request, or instruction directly input into the AI ​​model to guide the model's output. It can be a very simple instruction, such as "Please help me summarize the main points of this article", or a more complex task, such as "Please help me design a complex task with multiple steps and conditions." Prompts are specific instructions for interacting with the AI ​​system to trigger the model's response and affect the output.

2. The difference between prompt words and prompt word projects

? The English explanation of prompt word engineering is: Prompt Engineering

Prompt word engineering is a relatively new discipline that focuses on the development and optimization of prompt words, which can help users use large language models (LLMs) in various scenarios and research fields. It is not only about designing and developing prompt words, but also includes various skills and techniques for interacting with and developing large language models. Prompt word engineering involves selecting, writing, and organizing prompts to obtain the desired output, mainly including the following aspects:

• Prompt format: Determine the structure and format of the prompt, for example, question form, description form, keyword form, etc.
• Prompt content: Choose appropriate words, phrases, or questions to ensure the model understands the user's intent.
• Prompt context: Consider preceding or contextual information to ensure the model’s response is relevant to the previous conversation or situation.
• Prompt writing tips: Use clear, concise, and concise language to write prompts to accurately convey the user's needs.
• Prompt optimization: After trying different prompts, adjust and optimize the prompt based on the results to get a more satisfactory response.

3. Platform tools for writing prompt words

In fact, there are many tools for writing prompts, such as Feishu documents, which can be written directly, and PromptPort, which is also very useful. It is a creative AI prompt tool library designed specifically for ChatGPT services, allowing users to create, optimize and share prompts, which can help users interact with large models more efficiently. There are also prompts written by users for everyone to refer to, which is also very convenient to use.

IV. Case Study – Application and Implementation of E-commerce Prompt Words

1. Cross-border e-commerce (best seller copywriting optimization)

Requirements: Cross-border e-commerce operators need to convert Chinese product descriptions into English. The target users are the North American middle class. They need to avoid cultural taboos and embed SEO keywords while complying with the platform A+ page specifications.

Reference words:

You are a cross-border copywriting expert with 5 years of experience. Please optimize the following product description; [Enter original text] Requirements: 1. Embed keywords: eco-friendly packaging, lifetime warranty, FDA-certified 2. Avoid literal translation of Chinese rhetoric (such as "hot-selling" to "5000+ satisfied customers") 3. Use FAB structure: – Feature: Ceramic liner technology → converted to "12hr heat retention proven in lab tests" – Advantage: Save 50% of time → converted to "save 2hrs weekly for family time" 4. Generate A+ page modules: – Comparison Chart (comparison with competitor parameters) – Lifestyle Image Caption (highlight usage scenarios)

2. E-commerce operation (live broadcasting skills)

Demand: Generate live broadcast scripts for beauty products, design sales tactics + price anchors + interactive retention strategies

Reference words:

##New product [Bose Anti-aging Cream] live broadcast requirements: – Average order value: 298 yuan (gift sample valued at 198 yuan) – Competitive products: L’Oreal’s similar product is priced at 460 yuan – Target group: 30+ working women ##Please construct: 1. Price psychological warfare: design 3-level price anchors a) Comparison anchor: Medical beauty Thermage word 9,800 yuan b) Time anchor: The first 100 customers will receive a free full-size essence (valued at 298 yuan) c) Set anchor: 2 bottles will receive an instant discount of 150 yuan (need to note the code [Anti-aging CP] 2. Structure of the sales pitch (15-minute countdown): 00:00-03:00 [Pain point bombing]: “Sisters, have you seen the fine lines at the corners of my eyes? This is the overtime I contacted last week... (show close-up of my bare face)” 03:01-07:00 [Ingredient decryption]: “Laboratory data proves that our Bose concentration reaches ____% (show partial test report)” 07:01-15:00 [Ultimate order-forcing]: “There are only 37 orders left! Operations, please put the order IDs on the public screen! (Show real-time order ID scrolling)” 3. Three ways to retain customers through interaction: a) Stay mechanism: draw a free order every 5 minutes (comments are required [anti-aging check-in + city]) b) Curiosity trap: “Reveal the difference between the version removed by the big anchor in 3 minutes” c) Community diversion: “Take a screenshot of the current number of people online, and after the broadcast, go to customer service with the screenshot to get a sunscreen sample” ##Output requirements: – Use annotations to mark the conversion goals of each link (attract new customers/promote orders/retain customers) – Add anchor action instructions: such as [raise the test report at this time], [switch the camera to the laboratory scene] – Generate 3 strong guiding shopping cart copy (including emoji and action verbs)

Finally, prompt word engineering is crucial to achieving the best performance of language models. Through carefully designed prompt words, users can guide the model to generate more accurate, targeted and demand-oriented answers. Whether it is acquiring expertise in a specific field, creating texts in a specific style, or conducting efficient learning and research, it can greatly improve efficiency and quality and give full play to the huge potential of language models in various application scenarios.