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Prompt word engineering—a technology essential for AI application

Popularity：880 ℃/2025-03-13 15:44:40

introduction

Today, with the rapid development of artificial intelligence technology, Big Language Model (LLM) has become the core engine to promote technological innovation. However, how can these "smart" models be truly implemented in business scenarios and solve practical problems? The answer is often not in the scale of the parameter of the model itself, but in a seemingly simple but crucial technology—Prompt Engineering. Whether it is to let the model understand user intentions, call external tools, or generate structured data, prompt word engineering is like a key, unlocking the unlimited potential of the big model in real scenes. This article will deeply analyze the technical essence of prompt word engineering, and combine it with actual cases to reveal why it has become an indispensable core competitiveness in AI application development.

1. What is prompt word engineering

Prompt word engineeringIt is a technology that guides the big model to generate output results that meet the expectations by designing specific input instructions (Prompt). The core logic is:Transform human intentions into “language” that models can understand, thereby directing the model to complete complex tasks.

For example, if you want to extract key information from a piece of text, the developer does not need to train the model, but only designs such a prompt word:

Please parse the following text, extract "departure" and "destination", and output in JSON format:
 {"from": "", "to": ""}
 Text: I saw the ticket from Chengdu to Beijing, and the price increased by 500 yuan compared to last week.  
 The model will return: {"from": "Chengdu", "to": "Beijing"}.

This process seems simple, but in fact it requires accurate "dialogue" - this is the value of prompt word engineering.

2. Core technology of prompt word engineering

1. Instruction design: from blur to precision

  • Zero-Shot Prompting: Zero sample prompt, directly describe task objectives through natural language (such as "extract keywords")
  • Few-Shot Prompting: Few sample prompts, provide a small number of examples, so that the model can learn from one example and apply it to other examples. For example:
Example 1:
 Enter: I want to go to Shanghai from Shenzhen
 Output: {"from": "Shenzhen", "to": "Shanghai"}
 Example 2:
 Enter: When is the cheapest flight to New York?  
 Output: {"from": null, "to": "New York"}

The model learns the task rules through examples, and can correctly extract the destination even if it encounters unseen problems (such as "the ticket to Sanya is too expensive").

2. Structured output control

By clarifying the format requirements (such as JSON, Markdown), ensure that the model output can be directly parsed by the program. For example:

Answer in the following format:
 {"need_search": true, "keywords": ["keyword 1", "keyword 2"]}

This design allows the model to be seamlessly connected with the code, supporting multiple rounds of interactive processes.

3. Context dynamic management

In complex tasks, prompt words need to dynamically adjust the context. For example, in the online search scenario, the first round of prompt words requires the model to generate search keywords, and subsequent prompt words need to generate the final answer based on the search results.

3. Four key roles of prompt word engineering in AI application

1. Intent understanding: Let the model understand "human words"

User problems are often vague (such as "help me find a cheap destination"). Through the prompt word engineering, the problem can be broken down into structured instructions:

  1. Analyze user budgets and preferences;
  2. Call the price comparison API to get data;
  3. Generate a reason for recommendation.
    The model has thus been upgraded from a "chatbot" to a "business assistant".

2. Knowledge enhancement: Breakthrough of model memory bottlenecks (RAG technology)

The training data of large models has timeliness and professional limitations. Through search enhancement generation (RAG), the prompt word can direct the model:

  • Generate search keywords based on the question;
  • Integrate external knowledge base/search results into your answers. For example, in the medical consultation scenario, the model combines the latest paper data to generate diagnostic suggestions to avoid "serious nonsense".

3. Process Control: Building an Automated AI Agent

In complex tasks, prompt word engineering allows the model to play the role of "scheduler". For example, developing a travel planning agent:

If the user asks, “How to play in Japan on May Day?”
 → Prompt word requirements model:
    a. Generate destination keywords (such as "Tokyo Osaka Five Day Weather");
    b. Call the weather API and air ticket price comparison tool;
    c. Comprehensive results to generate a schedule.

The model connects multiple tool APIs through multiple rounds of prompt word interaction to achieve end-to-end automation.

4. Results Optimization: Reduce hallucinations and prejudice

Model fictitious content can be greatly reduced by binding prompt words such as “answer based on the following data”. For example:

Based on the 2023 financial report data (as follows), Tencent Cloud's revenue growth rate is summarized:
 Data:... (with specific numbers)
 Requirements: No speculation is added.

4. Practical cases: From prompt words to AI applications

Taking the "Internet search assistant" developed by a major manufacturer as an example, its core process is completely driven by prompt words:

  1. The first round of interaction: After the user asks a question, the prompt word requires the model to determine whether search is required and generate keywords.
  2. External call: The application performs a search and injects the result into the next round of prompt words.
  3. Final generation: The model generates answers based on search results and marks the source of the reference.
    In this process, the prompt word is like a "script", which strictly stipulates every step of the model, changing it from "free play" to "precise execution".

5. Future Outlook

With the complexity of AI application scenarios, prompt word engineering will show two major trends:

  1. Low code: Automatically generate prompt word templates through visualization tools to lower the development threshold.
  2. Dynamic evolution: Combined with model fine-tuning technology, self-optimization iteration of prompt words is achieved.

Conclusion

Prompt word engineering is not a "magic spell", but a new programming paradigm in the AI ​​era. It allows developers to direct big models to solve practical problems without delving into mathematical principles. As a technical expert from a major manufacturer said:"In the next decade, people who can write prompt words may be more popular than people who can write Python."Mastering this technology is to hold the key to opening the door to AI applications.

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