Tips on ten design patterns in engineering

We can provide details, rules and guidance to elicit more targeted output. By providing detailed details, rules, and guidance, we can enhance a model's performance and influence its output. We can make the prompt words more specific by providing details, rules, and guidance for more accurate output.

Design patterns are general, repeatable solutions to common problems. Each design pattern is by no means a complete solution that can be directly applied to a problem, but rather a template or framework that can be used to better build solutions that apply best practices. Design patterns are widely used in the field of object-oriented programming. The veteran coder tried to summarize 10 common design patterns in improvement projects.

1. Character model

The character model is implemented by inputting a specific personality or speaking tone into the language model. By defining different roles, we can control the style and way of generating text to adapt to various application scenarios. Here are some examples:

• Customer Support: In the customer support world, a friendly, patient role may be more effective at communicating with customers, resolving issues, and providing assistance. For example, when a customer asks a question, the language model can respond in a polite and approachable tone and provide a clear and concise solution, thereby enhancing customer satisfaction.
• Storytelling: In fictional stories or creative writing, different characters may need to have different tones and emotional expressions. For example, a humorous character might use humor and exaggeration to tell a story, while a serious character might use a serious and calm tone.
• Educational content: In the field of education, language models can play a variety of different roles to better adapt to the needs and learning styles of different learners. For example, for children's educational content, the model can use a relaxed and lively tone to attract their attention, while for professional and technical courses, the model can use a more formal and rigorous tone to convey knowledge.

Through different character models and language models, flexibility and personalized expression can be increased, thereby improving the interactive experience with users and playing a role in various application scenarios greater effect.

2. Recipe Mode

Recipe mode provides a valuable method for tasks that require detailed and sequential instructions. It can generate text for large models, such as tutorials, process documentation or installation guides. This model requires detailed and sequential instructions, such as tutorials, process documentation, or installation guides. For example, you can use this pattern to generate tutorials, procedural documentation, or configuration guides.

• Tutorial: Imagine you are writing a tutorial article that introduces readers to how to learn a certain skill, such as learning to program or learning to draw. With recipe mode, you can provide clear steps and guidance so readers can gradually understand and practice what they learn, making it easier to master new skills.
• Process documentation: In industrial production or scientific experiments, it is often necessary to write detailed process documents to record and share operating steps. Using recipe mode, you can describe each step step by step, ensuring readers can accurately reproduce the operation process, thereby increasing work efficiency and reducing the possibility of errors.
• Creating Assembly Guides: In manufacturing, producing assembly guides is crucial for factory workers. You can provide detailed instructions for each assembly step, including required tools, materials, and procedures to ensure the product assembles correctly and meets quality standards.

Through this mode, large models can provide coherent and structured text output, allowing readers to easily understand and practice the guidance in various application scenarios. Achieve more efficient work and study.

3. Reverse query mode

In reverse query mode, the large model is required to work in a special way: first, it receives an input or The response serves as a starting condition and is then asked to produce the most appropriate query or input to produce a specific output. This technology can be used not only in question and answer scenarios, but also in various other situations. For example, in the field of search engines, large models can transform users' search content into the most suitable search results through reverse queries. This technology has wide applications in text generation, natural language processing, and other fields.

• Smart Assistant: Suppose you are talking to a smart assistant and you ask a question, but you want to drill down to learn more about it. In this case, reverse query mode can be applied. Your smart assistant can generate a response based on your question and then ask you if you want to know more about it, leading to deeper queries.
• Search Engine Optimization: In web content creation, reverse query mode can be used to optimize search engine results. Let’s say you are a webmaster and want your website to rank higher for a specific search query. You can use reverse lookup patterns to create content that ensures your website appears in relevant query results in search engines.
• Personalized recommendation system: In the field of e-commerce or content recommendation, the reverse query mode can be used for personalized recommendation systems. The system can generate some output based on the user's behavior and preferences, and then generate corresponding queries based on these outputs to provide more personalized and accurate recommendations.

Through the reverse query mode, large models can generate corresponding queries or inputs based on specific input and output, thereby better meeting user needs and improving system performance. and user experience.

4. Output automation mode

Automation mode is a way to use indicator words to normalize large models to generate structured or formatted output to achieve repetitive tasks. automation. For example, it can be used in the following scenarios:

• Report Generation: In a corporate environment, sales reports need to be generated on a daily basis. Through the output automation mode, sales data can be input into the language model and then a report in a predefined format can be generated, eliminating the time and labor of manual report writing.
• Abstract generation: In academic research, it is necessary to extract information from a large amount of literature and generate abstracts. Using the output automation mode, large models can automatically generate document summaries based on keywords or topics given by the user, greatly improving the efficiency of processing large amounts of text.
• Response generation: In the field of customer service, there is often a need to respond quickly to customers' frequently asked questions. Through the output automation mode, the language model can automatically generate appropriate responses based on the keywords or classification of the question, thereby improving the efficiency and accuracy of customer service.
• Code Writing: Writing repetitive code is a common task for developers. The output automation mode can be used to instruct the language model to automatically generate code snippets based on the preferred coding language selected by the user, thereby speeding up the development process and reducing coding errors.

Automated mode can greatly improve work efficiency and accuracy, especially in the fields of mining and data analysis, content generation and software development.

5. Chain of Thought Mode

Chain of Thought (CoT) mode is a technology that guides the generation of large models according to a specific reasoning or argumentation path. This mode is extremely valuable for creating persuasive articles, reviews, or complex discussions, since logical flow is a key element in building its credibility and understandability. Here are some examples:

• #Commentary Article: When writing an opinion piece, you must ensure the logical coherence and rigor of your arguments. The thought chain model can guide the language model to generate arguments, rebuttals and conclusions according to the logical structure of the debate, thereby making the article more persuasive and logical.
• Scientific papers: In the field of science, papers must be based on scientific reasoning to ensure the credibility and repeatability of experimental results and conclusions. The thinking chain model can help language models follow the logical chain of scientific reasoning, from problem statement to experimental design to result analysis, to generate papers that meet scientific standards.
• Defence: In legal scenarios, lawyers must provide a strong defense in support of their clients. The thinking chain model can guide the language model to generate defense words according to legal logic, including stating facts, citing legal provisions, putting forward arguments and refuting the opponent's views, thereby providing a strong defense for the case.

Through the thinking chain mode, the large model can generate text according to the path of logical thinking, making it more coherent, persuasive and understandable, so that it can be used in various fields play an important role in.

6 Graph-assisted mode

Graph-assisted mode is a method that uses existing knowledge to enhance prompts, thereby helping large language models generate more accurate output results. This model improves the model's understanding and output quality by combining a knowledge graph or domain expertise with the model to provide more background information and context. Here are some examples:

• Medical Diagnosis: In the medical field, graph-assisted mode can be used to help language models better understand clinical cases or medical reports. By combining the medical knowledge graph and patient history, the model can generate more accurate diagnostic recommendations or treatment plans.
• Intelligent customer service: In the field of customer service, the graph assistance mode can be used to improve the response quality of the intelligent customer service system. Models can use industry domain knowledge graphs to provide customers with more professional and accurate solutions, thereby improving customer satisfaction.
• Legal consultation: In the legal field, graph-assisted mode can help language models better understand legal documents or case details. By integrating legal knowledge graphs and case regulations, the model can provide more accurate legal advice or legal analysis, helping lawyers and legal professionals better handle cases.

Through the graph-assisted mode, large models can use rich knowledge resources to enhance the accuracy and reliability of their output, thereby playing a greater role in various application scenarios. effect.

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7. Fact Checking Mode

To reduce the risk of generating false or misleading information, Fact Check mode prompts large language models to validate their output against reliable external sources or databases. This model encourages large models to provide supporting evidence to prove the credibility of their answers, thus promoting accurate results. Here are some examples:

• #News reporting: In the field of journalism, fact-checking patterns can help language models verify the accuracy of news events. Models can cite trusted news organizations or official sources to support the facts they report, thereby reducing the spread of false information.
• Academic Papers: In academic writing, fact-checking mode ensures that the language model cites peer-reviewed research or authoritative data to support its argument. This helps ensure the accuracy and credibility of your paper.
• Medical Consulting: In the medical field, fact-checking mode can help language models verify the accuracy of medical information. Models can cite authoritative medical journals or medical databases to support the medical advice or explanations they provide, thereby reducing the risk of misleading information.

Through fact checking mode, large models can provide more reliable and accurate output, thereby enhancing their credibility and practicality in various application scenarios.

8. Reflective Mode

Reflective mode encourages large models to critically evaluate the text they generate. This pattern prompts large models to examine potential biases or uncertainties in their outputs. Here are some examples:

• #Social media comments: On social media, language models may be used to generate comments or replies. In reflection mode, the model should reflect on whether the comments it generates contain discriminatory remarks or misleading information, and avoid these problems as much as possible.
• News reports: In news reports, language models may be used to write articles or provide comments. In reflective mode, the model should review whether the content it generates is accurate, objective, and potentially influenced by external factors.
• Educational Materials: In the field of education, language models may be used to generate educational materials or answer questions. In reflective mode, the model should consider whether the content it generates is useful for learning, whether it contains errors or subjective biases, and whether it requires further verification or correction.

Through reflection mode, large models can more consciously evaluate their output, avoid inappropriate remarks or misleading information, and provide more responsible and credible answers.

9. Question Refinement Mode

Question Refinement Mode is an iterative approach in which an input query or prompt is continuously refined based on feedback from the language model. By analyzing the model's response to different prompts, developers can fine-tune queries to improve model performance. Here are some examples:

• Search Engine Optimization: Suppose you are a webmaster and you want to improve your website's ranking in search results through search engine optimization. You can use question refinement mode to continuously optimize your search queries, adjusting keywords, sentence structure, etc. based on feedback from the language model to improve your website's visibility in search engines.
• Voice Assistant: In a voice assistant application, users may ask various questions or instructions. Question refinement mode allows developers to analyze the language model's response to different queries and then adjust user interface or system settings to improve the accuracy and responsiveness of the voice assistant.
• Natural language processing applications: In natural language processing applications, such as chatbots or intelligent customer service systems, the question refinement mode can be used to continuously optimize the model's response. Developers can analyze the model’s responses to different user questions and then fine-tune the model to make it smarter and more adaptable.

Through the question refinement mode, developers can interact with the language model and continuously improve the performance and effect of the model, thereby providing a better user experience and more accurate results.

10. Partial Rejection Pattern

Sometimes, an AI model may answer “I don’t know” or refuse to generate output when faced with a complex query. To handle this situation more efficiently, "Circuit Break Deny Mode" was introduced. The goal of this model is to train the model to provide useful responses or partial answers when faced with difficult or inaccurate answers, rather than outright rejection. Here are some examples:

• Chatbot: When a user asks a chatbot a question that is beyond its knowledge, the traditional approach might be to simply respond with “I don’t know ". However, with circuit-breaker rejection mode, the chatbot can try to provide relevant information or suggestions based on existing information or context, and even partial answers can provide some help to the user.
• Search Engine: When a search engine cannot find an exact match for a user query, it often displays a message stating that no results were found. However, with this model, the search engine can try to provide relevant content based on the intent of the user's query, even if it does not have a complete answer, it can provide some relevant information or guidance.
• Voice assistant: In a voice assistant application, when the user asks a question that is beyond the scope of the voice assistant’s knowledge, the voice assistant can adopt the circuit-breaking rejection mode and try to provide useful tips or suggestions. To help the user better understand or solve the problem, rather than simply answering "I don't know."

Through this mode, the artificial intelligence model can handle complex situations more flexibly and intelligently, improving its adaptability and user experience.

Not over

Tips The engineering design pattern is a powerful tool that can better utilize the capabilities of large models. The patterns introduced in this article can help improve the overall quality of a given large model. By leveraging these patterns, we can tailor the output for specific use cases, identify and correct errors, and optimize prompts for more accurate and insightful responses. As AI technology continues to evolve and new models emerge, prompt engineering may still be one of the key factors in creating more reliable and intelligent AI conversational systems.

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