{"id":48131,"date":"2026-01-02T09:48:07","date_gmt":"2026-01-02T01:48:07","guid":{"rendered":"https:\/\/www.1ai.net\/?p=48131"},"modified":"2025-12-31T15:56:00","modified_gmt":"2025-12-31T07:56:00","slug":"ai%e6%8f%90%e7%a4%ba%e8%af%8d%e6%80%8e%e4%b9%88%e7%94%a8%ef%bc%9f%e6%95%99%e4%bd%a0%e5%86%99%e5%87%ba%e5%ae%8c%e7%be%8e%e7%9a%84prompt%ef%bc%88%e6%8f%90%e7%a4%ba%e8%af%8d%ef%bc%89","status":"publish","type":"post","link":"https:\/\/www.1ai.net\/en\/48131.html","title":{"rendered":"What's the AI tip? Teach you to write perfect Prompt"},"content":{"rendered":"<p><strong><a href=\"https:\/\/www.1ai.net\/en\/tag\/prompt\" title=\"[View articles tagged with [Prompt]]\" target=\"_blank\" >Prompt<\/a>What is it<\/strong><\/p>\n<p>Many people think that \u201cPrompt = Directive to AI\u201d ignores Prompt as a set of people working with AI. It is not only about \u201cdoing what\u201d, it is about defining \u201cwhy\u201d, \u201cdo what\u201d, \u201cdo what\u201d, and what\u201d, which is essentially about translating people's vague needs into AI's understandable and implementable structural tasks\u3002<\/p>\n<p>From a technical logic, Prompt ' s central role is to guide LLM in the mobilization of pre-training knowledge through input sequences, to focus on specific mission scenarios and to generate outputs that meet expectations\u3002<\/p>\n<p>LLM itself has no experience in the world of work, nor can it interpret the meaning of it, and the leadership\u2019s offer to his subordinates to make a sea trip may imply that he was more interested in the South-East Asian market than his previous boss had mentioned, but AI would only produce generic content literally. So Prompt's core value is to eliminate poor information (both between human needs and AI understanding and between mission objectives and implementation standards). I'm not sure<\/p>\n<p><strong>Some errors on Prompt<\/strong><\/p>\n<p><strong>Mistake 1: The more complex, the more professional, the more overstretched the terminology and format<\/strong><\/p>\n<p>It was felt by many that Prompt needed to include industry standards, professional terms, complex labels to reflect professionalism. For example, AI is required to \u201cmanufacture the enforcement system with an ISO 8402 terminological analysis, following the ISA-95 standard\u201d, but the actual result is often to export the obscure theoretical text of AI, which is completely different from the actual use scene\u3002<\/p>\n<p><strong>Truth:<\/strong>Prompt's professionalism is not in complexity, but in precision matching. The current LLM (e.g. Claude 4, GPT-4) has a strong understanding of the natural language and can achieve the same or even better results without the need for XML labels, specialized terminology and clear titles, segments, and guides. The real professional Prompt is to fit the content of the AI output with the scene, not to make Prompt itself as obscurant\u3002<\/p>\n<p><strong>Mistake 2: Just say what to do and ignore hidden demands and constraints<\/strong><\/p>\n<p>USERS CONSIDER IT CLEAR ENOUGH FOR AI TO WRITE AN OUTREACH PROJECT AND FOR AI TO DO A DATA ANALYSIS, BUT IGNORE KEY MESSAGES SUCH AS WHO THE TARGET AUDIENCE IS, WHAT THE CORE POINTS ARE, AND WHAT THE OUTPUT FORMAT IS\u3002<\/p>\n<p><strong>Truth:<\/strong>LLM HAS NO SO-CALLED INDUSTRY COMMON SENSE AND NO DEFAULT SETUP. FOR EXAMPLE, A PROGRAMME MAY BE A STRATEGIC VERSION FOR CEOS, OR A LAND-BASED VERSION FOR SALES; A TWEET MAY BE DIRECTED AT TECHNOLOGY DECISION MAKERS OR FRONT-LINE OPERATORS ... IF THESE HIDDEN NEEDS ARE NOT CLEAR, AI CAN ONLY GUESS BLINDLY, AND THE OUTPUT WILL CERTAINLY NOT MEET YOUR PSYCHOLOGICAL EXPECTATIONS\u3002<\/p>\n<p><strong>Error 3: One key generation is the end point, rejecting iterative optimization<\/strong><\/p>\n<p>Many expect a perfect result from one input, and if the output does not match expectations, they will assume that AI is no good and will not spend time optimizing Prompt. In fact, Prompt's optimization process is essentially a process of gradual clarity of needs, not only to make AI more aware of you, but also to make your core claims more clear to you\u3002<\/p>\n<p><strong>Truth:<\/strong>In some work scenarios, such as client programmes, industry reports, the process itself needs to be repeated. AI is just a tool for you to speed up. For example, when you wrote the ERP programme with AI, Prompt was only given a generic framework for the first time, and then added \u201cthe target customer is a food-processing enterprise, with a focus on a batch-retroactive module\u201d, the alignment was increased and the programme was further strengthened by adding \u201cciting three client case data\u201d\u3002<\/p>\n<p><em><strong>Bottom logic of Prompt capabilities: structured thinking + precision expression\u3002<\/strong><\/em><\/p>\n<p><strong>The essence of Prompt capabilities requires users to have:<\/strong><\/p>\n<ul>\n<li><strong>Demand for dismantling capacity:<\/strong>Dismantling vague targets into specific, implementable sub-tasks<\/li>\n<li><strong>Structured expression:<\/strong>(A) ORGANIZE INFORMATION WITH CLEAR LOGIC TO ENABLE AI TO QUICKLY CAPTURE THE CORE<\/li>\n<li><strong>The power of the scene:<\/strong>Define the standards and style of the output from the point of view of using the scene and the audience<\/li>\n<li><strong>Reciprocal optimization:<\/strong>The instructions are continuously adjusted through test feedback to achieve optimal results\u3002<\/li>\n<\/ul>\n<p>It also explains why it is difficult to use AI when it comes to leadership that is not even clear about instructions to subordinates, because the quality of Prompt ultimately depends on the depth of the user ' s thinking and expression\u3002<\/p>\n<p><strong>Bottom logic for building efficient Prompt<\/strong><\/p>\n<p><strong>1.<\/strong><strong>Roles, needs, scenes, targets<\/strong><\/p>\n<p>Prompt's first task is to make it clear who you are, who you are, what you're doing, what you're doing, what you're going to do, what you're going to achieve<\/p>\n<ul>\n<li><strong>Role:<\/strong><\/li>\n<li>(a) Clearly define the role of the input (e.g., you are a sales member of HR SaaS) and determine your position<\/li>\n<li><strong>Audience alignment:<\/strong>IDENTIFYING RECIPIENTS OF OUTPUTS (E.G. MANUFACTURING HRD, INTERNET PRODUCT MANAGERS, GOVERNMENT DECISION MAKERS) AND DETERMINING THE PROFESSIONAL DEPTH AND LANGUAGE STYLE OF CONTENT<\/li>\n<li><strong>scene alignment:<\/strong>(a) Clearly use the scenes (e.g., client selection presentations, internal wrap-up sessions, industry community outreach) to determine the focus and presentation of content<\/li>\n<li><strong>Target alignment:<\/strong>Clarify core objectives (e.g., convey product values, address specific issues, provide a basis for decision-making) and determine core logic and key messages of content\u3002<\/li>\n<\/ul>\n<p><strong>The error demonstration:<\/strong>An article about HR SaaS products\u3002<\/p>\n<p><strong>Error point:<\/strong>WITHOUT CLEARLY IDENTIFIED AUDIENCES, SCENES AND TARGETS, AI CAN ONLY OUTPUT A GENERIC PRODUCT DESCRIPTION AND CANNOT BE USED DIRECTLY\u3002<\/p>\n<p><strong>The correct demonstration:<\/strong>Now that you are a highly professional HR SaaS marketing firm, please write a HR SaaS product promotion for manufacturing HRD for industry community orientation, with the core objective of highlighting the function of \"task data and production scheduling\" and attracting target users to a free trial link\u3002<\/p>\n<p><strong>Analysis:<\/strong>AUDIENCES, SCENES, OBJECTIVES ARE CLEAR AND AI OUTPUTS ARE MORE TARGETED\u3002<\/p>\n<p><strong>2.<\/strong><strong>STRUCTURED EXPRESSION TO LOWER AI'S UNDERSTANDING COSTS<\/strong><\/p>\n<p>LLM processes structured information much more efficiently than fragmentation information. When constructing Prompt, use the logical framework of total-point-total-dimensional dismantling to enable AI to quickly capture the core command:<\/p>\n<ul>\n<li><strong>Core command prefix:<\/strong>(a) To make it clear at the beginning what to do and avoid redundant information interference<\/li>\n<li><strong>Here's an idea:<\/strong>Distinguishing \" core tasks \" , \" background information \" and \" output format \" with headings, serial numbers and subparagraphs<\/li>\n<li><strong>The logic is clear:<\/strong>The logical chain of the mission is clearly presented with the words \u201cbecause ...\u201d \u201cfirst ... and last ...\u201d (or 1, 2 and 3 ...)\u3002<\/li>\n<\/ul>\n<p><strong>The error demonstration:<\/strong>HELP ME ORGANIZE THE Q3 CLIENT FEEDBACK, SEE IF THERE'S ANY PROBLEM AND ADVISE THE PRODUCT DEPARTMENT, PREFERABLY WITH DATA SUPPORT\u3002<\/p>\n<p><strong>Error point:<\/strong>INFORMATION FRAGMENTATION MAKES IT DIFFICULT FOR AI TO COMBE PRIORITIES\u3002<\/p>\n<p><strong>The correct demonstration:<\/strong><\/p>\n<p>\u2022 MANDATE: COLLATE CLIENT FEEDBACK ON Q3 CRM PRODUCTS AND GENERATE DEMAND ANALYSIS REPORTS\u3002<\/p>\n<p>BACKGROUND: USED FOR THE PRIORITIZATION OF THE REQUIREMENTS OF THE PRODUCT DEPARTMENT VERSION V.2.3<br \/>\nCore logic: first, a breakdown of feedback by \u201cfunctional optimization, system stability, service support\u201d, then a breakdown of the percentage of clients per type of feedback, followed by recommendations for improvements to Top3 distress points<br \/>\nData requirements: Number and percentage of client feedback for each requirement\u3002<\/p>\n<p><strong>Analysis:<\/strong>THE STRUCTURE IS CLEAR, AND AI CAN BE IMPLEMENTED IN A LOGICAL STEP-BY-STEP MANNER\u3002<\/p>\n<p><strong>3.<\/strong><strong>The scene fits the actual context<\/strong><\/p>\n<p>Okay, Prompt not only finishes the job, but also adapts to the scene. For example, also in the case of product presentations, content for technology decision makers needs to highlight technical architecture and integration capabilities, content for business decision makers needs to highlight ROI and business value, and content for front-line operators needs to highlight ease of use and efficiency improvements<\/p>\n<p>At the heart of the fit-for-the-scenes is the ability to \"replace\":<\/p>\n<ul>\n<li><strong>Think about the core demands of using the scene:<\/strong>In the case of client selection, there is a need to highlight the \u201cdiscretionary advantage\u201d; in-house training is a need to highlight the \u201coperational steps\u201d<\/li>\n<li><strong>Thinking about the level of awareness of the audience:<\/strong>In the case of non-skilled persons, avoiding professional terminology; in the case of industry experts, the professional details could be deepened as appropriate<\/li>\n<li><strong>How to use content:<\/strong>FOR PPT PRESENTATIONS, THEY NEED TO BE CONCISE AND REFINED; FOR WRITTEN REPORTS, THEY NEED TO BE DETAILED AND RIGOROUS\u3002<\/li>\n<\/ul>\n<p><strong>Case in point:<\/strong><\/p>\n<p>THE MES SYSTEM PRESENTATION FOR THE INSPECTOR GENERAL OF MANUFACTURING, WHICH FOCUSES ON THREE CORE DEMANDS FOR PRODUCTION EFFICIENCY IMPROVEMENT, QUALITY RISK CONTROL, EQUIPMENT OPTIMIZATION, AND VAGUE FORMULATIONS SUCH AS A REDUCTION IN THE TIME SPENT ON EQUIPMENT BY 201 TP3T AND A REDUCTION IN THE BAD RATE 151 TP3T TO REPLACE THE STRONG FUNCTIONALITY OF QUANTITATIVE DATA, ALIGNS THE WORK FOCUS OF THE CHIEF PRODUCTION OFFICER\u3002<\/p>\n<p><strong>4.<\/strong><strong>Over and over again<\/strong><\/p>\n<p>Prompt, which was written for the first time, is almost impossible to perfect, and the efficient use of AI requires the establishment of a test-feedback-optimization loop:<\/p>\n<ul>\n<li><strong>Preliminary test:<\/strong>The first version of the output is generated by simple Prompt to determine whether AI ' s understanding of core needs is accurate; (testing)<\/li>\n<li><strong>Problem positioning:<\/strong>Analyse the deviations of the output (e.g., failure to highlight core functions, inconsistent language styles, lack of data support) and locate missing information from Prompt<\/li>\n<li><strong>Precision optimization:<\/strong>TARGETED ADDITIONAL INFORMATION (E.G., HIGHLIGHTING XX FUNCTIONALITY, MORE FORMAL LANGUAGE STYLE, CITING DATA FROM THREE CLIENT CASES) RATHER THAN REWRITING IT AS A WHOLE<\/li>\n<li><strong>Version comparison:<\/strong>Keep multiple versions of Prompt and output and see which formulation is more in line with AI ' s understanding logic\u3002<\/li>\n<\/ul>\n<p><strong>Practising layers: basic to progressive Prompt<\/strong><\/p>\n<p><strong>1.<\/strong><strong>Basic approach<\/strong><\/p>\n<p>At the heart of the basic skills are \u201cclear things\u201d and apply to most simple tasks (e.g., writing short papers, collating data, answering questions) without complex logical design to produce the results quickly\u3002<\/p>\n<p><em><strong>Skills 1: Demand dismantling - translating vague needs into specific tasks<\/strong><\/em><\/p>\n<p><strong>Core logic:<\/strong>DISMANTLING \u201cDOING WHAT\u201d TO \u201cVERB + OBJECT + CONSTRAINT\u201d TO ALLOW AI TO SPECIFY \u201cWHAT ACTIONS ARE CARRIED OUT, WHAT CONTENT IS ADDRESSED AND WHAT RULES ARE FOLLOWED\u201d:<\/p>\n<ul>\n<li><strong>Verb:<\/strong>(b) Clarify core actions (e.g., analysis, writing, collating, comparison, generation, etc.) and avoid the expression of vague words<\/li>\n<li><strong>Object:<\/strong>CLEAR CORE CONTENT (E.G. Q3 SALES DATA, DIGITAL TRANSFORMATION PAIN POINTS FOR MANUFACTURING, \u201cERP PRODUCT CORE FUNCTIONS, ETC.)<\/li>\n<li><strong>Binding:<\/strong>Clear border conditions (e.g. region-by-region, highlighting three core pain points, not involving technical details, etc.)\u3002<\/li>\n<\/ul>\n<p><strong>hands-on template: [verb] [object]<\/strong><\/p>\n<p>Practising template: [verb] [object]<\/p>\n<p>Binding:<\/p>\n<p>[dimensional 1, e.g. audience\/scenario]<\/p>\n<p>[Drive 2, such as core elements]<\/p>\n<p>[dimensional 3, e.g. format\/long]<\/p>\n<p><strong>Case in point:<\/strong>This post is part of our special coverage Global Development 2011<\/p>\n<p>Binding:<\/p>\n<p>1. Audience: business owners who do not understand technology<\/p>\n<p>2. Core elements: highlighting the cost advantages and difficulties of digital transformation<\/p>\n<p>Format: 3 paragraphs, each not exceeding 150 words, with a link to the transformation self-assessment questionnaire at the end\u3002<\/p>\n<p><em><strong>SKILLS 2: CONTEXT COMPLETION - PROVIDE KEY BACKGROUND FOR AI<\/strong><\/em><\/p>\n<p><strong>Core logic:<\/strong>THE QUALITY OF LLM OUTPUT DEPENDS ON THE BACKGROUND INFORMATION ENTERED, AND IT IS NECESSARY TO CLARIFY \u201cWHY\u201d, \u201cWHAT LIMITS\u201d, \u201cWHAT REFERENCES\u201d, TO HELP AI UNDERSTAND THE OPERATIONAL VALUE AND BOUNDARIES OF THE MISSION:<\/p>\n<ul>\n<li><strong>Operational background:<\/strong>Describe the origin of the mandate (e.g., the need for new marketing strategies to respond to competitive shocks)<\/li>\n<li><strong>Conditions of restraint:<\/strong>DESCRIBE THE LIMITS OF THE MANDATE (E.G. A BUDGET OF NOT MORE THAN HALF A MILLION, A THREE-MONTH IMPLEMENTATION CYCLE, COMPLIANCE REQUIREMENTS IN LINE WITH GDP R)<\/li>\n<li><strong>References:<\/strong><\/li>\n<li>PROVIDE RELEVANT DATA, CASES, HISTORICAL RESULTS (E.G. REFERENCE TO Q3 SALES DATA, USE OF XX CLIENT SUCCESS CASES)\u3002<\/li>\n<\/ul>\n<p><strong>The error demonstration:<\/strong>Collating client feedback and generating reports\u3002<\/p>\n<p><strong>Error point:<\/strong>THE BACKGROUND IS NOT SPECIFIED, AND AI MAY SEQUENCE FEEDBACK IN TIME AND HAVE NO REFERENCE VALUE\u3002<\/p>\n<p><strong>The correct demonstration:<\/strong>Collating client feedback on Q3 HR SaaS products to generate demand analysis reports. Operating background: used for the prioritization of needs for version V2.3 of the Product Department; binding conditions: focus only on three modules: attendance management, pay accounting, and staff training; reference information: Q3 collects 120 feedbacks, of which 58 are for attendance, 32 for pay module and 30 for training module (* Note: these feedbacks are uploaded here)\u3002<\/p>\n<p><em><strong>Skills 3: Format Definition - Clarify the Structure and Presentation of Output<\/strong><\/em><\/p>\n<p><strong>Core logic:<\/strong>PRE-DEFINED OUTPUT FORMATS TO AVOID THE CONTENT GENERATED BY AI REQUIRES A SECONDARY SORTING PROCESS TO ENHANCE EFFICIENCY\u3002<\/p>\n<p>Common formats include:<\/p>\n<ul>\n<li><strong>Text format:<\/strong>e.g. Markdown, breaklist, paragraph, dialogue<\/li>\n<li><strong>Structure format:<\/strong>e.g. tables, JSON, PPT outline, flow chart (in Mermaid syntax)<\/li>\n<li><strong>Special format:<\/strong>SUCH AS MAIL, REPORTS, PROGRAMMES, CASES, FAQ\u3002<\/li>\n<\/ul>\n<p><strong>hands-on template:<\/strong><\/p>\n<p>Output format requirements:<\/p>\n<p>Structure: 4 parts by Pain Point - Program - Case - Impact<\/p>\n<p>Format: Markdown, 1 TP5T#, 1 TP5T1T#<\/p>\n<p>Data presentation: Core data with rough labels and cases with tables (customer name - industry - effects)\u3002<\/p>\n<p><strong>Case in point:<\/strong>ANALYSIS OF THE COMPETITION DIFFERENCES IN THE MAINSTREAM RM PRODUCTS IN PARAGRAPH 3, OUTPUT FORMAT:<\/p>\n<p>1. Form, listing: product name - core function - price range - applicable scenario - competitive advantage<\/p>\n<p>2. A 300-word summary is added below the table to recommend options for small and medium-sized manufacturing enterprises\u3002<\/p>\n<p><em><strong>Skills 4: Example Guidance - Solve style\/format puzzles with \"Small Sample Tips\"<\/strong><\/em><\/p>\n<p><strong>Core logic:<\/strong>FOR TASKS FOR WHICH THE FORMAT REQUIRES A COMPLEX AND SPECIFIC STYLE (E.G., CASE WRITING, QUESTIONNAIRE DESIGN, CODE GENERATION), IT IS MORE EFFICIENT TO DIRECT AI WITH \u201c1-3 EXAMPLES\u201d THAN SIMPLY DESCRIBE THE FORMAT\u3002<\/p>\n<ul>\n<li><strong>Example requirements:<\/strong>Simplicity, with emphasis on \u201cstructure\/style\/ logic\u201d rather than full content<\/li>\n<li><strong>Example fit:<\/strong>The examples need to be consistent with the target mission scenario (e.g., the goal is to write retail trade cases, and the examples are also, to the extent possible, retail trade)<\/li>\n<li><strong>Quantity control:<\/strong>one example would solve most of the problems and would increase the number of complex missions to two to three, avoiding excessive token\u3002<\/li>\n<\/ul>\n<p><strong>The error demonstration:<\/strong>Write a client case of HR SaaS products for the retail trade\u3002<\/p>\n<p><strong>Error point:<\/strong>WITHOUT A CLEAR FORMAT, AI OUTPUT CASES MAY BE STRUCTURALLY CONFUSED AND LACK DATA SUPPORT\u3002<\/p>\n<p><strong>The correct demonstration:<\/strong>Writing of the retail trade HR SaaS product customer case (customers: supermarket chains, 10 stores, 300 employees) following the following example\u3002<\/p>\n<p>Example: Client case: a car spare parts business (500 people, 200 million per year)<\/p>\n<p>PAINFUL: PRODUCTION IS NOT SYNCHRONIZED WITH HR SCHEDULE DATA, WITH A VACANCY RATE OF 151 TP3T; MANUAL ACCOUNTING ATTENDANCE TAKES 3 DAYS PER MONTH. SOLUTIONS: THE HR SYSTEM IS USED TO ENABLE SCHEDULING AND ATTENDANCE TO AUTOMATICALLY GENERATE REPORTS\u3002<\/p>\n<p>EFFECTS: DISMISSAL RATE REDUCED TO 51 TP3T, ATTENDANCE TIME REDUCED TO ONE DAY AND ERROR RATE TO ZERO\u3002<\/p>\n<p>THE CORE MESSAGE OF THE NEW CASE IS THAT THE PAIN IS THAT \u201cDOOR SHOP STAFF ARE FREQUENTLY SCHEDULED AND HR COMMUNICATION COSTS ARE HIGH; OVERTIME ACCOUNTING IN THE FRESH SECTOR IS COMPLEX\u201d, AND THAT THE SOLUTION IS \u201cDOOR SHOP SELF-SCHEDULED + AUTOMATIC OVERTIME ACCOUNTING\u201d\u3002<\/p>\n<p><strong>2.<\/strong><strong>Progress Policy<\/strong><\/p>\n<p>For complex tasks (e.g., preparation of industry white papers, multi-dimensional analysis, annual programming), basic skills alone are difficult to meet, and a more advanced Prompt strategy is required to guide AI to in-depth thinking and logic\u3002<\/p>\n<p><em><strong>STRATEGY 1: THE CHAIN OF THOUGHT - LET AI REASON<\/strong><\/em><\/p>\n<p><strong>Core logic:<\/strong>THE COMPLEX TASK IS ESSENTIALLY A \u201cMULTI-STEP PROCESS OF REASONING\u201d, AND THE CHAIN OF THOUGHT LEADS TO A LOGICAL STEP-BY-STEP ANALYSIS BY AI BY MEANS OF A CLEAR \u201cDOCTIVE STEP\u201d, AVOIDING THE OUTPUT OF PARTIAL OR LEAPFROGGING CONCLUSIONS\u3002<\/p>\n<ul>\n<li><strong>Step design<\/strong>(i) Designing the logical steps of the business (e.g., competitive analysis: identifying functional differences in the matching of user needs to collect feedback from clients to summarize competitive advantages)<\/li>\n<li><strong>Step constraints:<\/strong>EACH STEP CLEARLY DEFINES \u201cWHAT TO DO\u201d, \u201cOUTPUT WHAT\u201d AND AVOIDS AI SKIPPING CRITICAL LINKS<\/li>\n<li><strong>Direct completion:<\/strong>IF THE AI REASONING IS INCOMPLETE, IT CAN BE SUPPLEMENTED BY GUIDANCE (E.G., IN THE SECOND STEP, THE DIFFERENCE BETWEEN THE PARTIES IN THE IMPLEMENTATION CYCLE)\u3002<\/li>\n<\/ul>\n<p><strong>For example:<\/strong><\/p>\n<p>FIRST OF ALL, GIVE A BASIC DESCRIPTION OF THE PRODUCTS OF YOUR COMPANY ' S ERP (OR UPLOAD THEM DIRECTLY FOR FEED)<\/p>\n<p>ANALYSIS OF THE DIFFERENCE BETWEEN OUR ERP PRODUCTS AND THE COMPETITION OF K\/3 WISE IN SMALL AND MEDIUM-SIZED MANUFACTURING ENTERPRISES, WITH GRADUAL REASONING IN THE LINE OF THOUGHT: FIRST STEP: IDENTIFICATION OF THE CORE NEEDS OF SMALL AND MEDIUM-SIZED MANUFACTURING ENTERPRISES (ADDITIONAL INFORMATION: BASED ON Q3 STUDIES, WITH A FOCUS ON COST ACCOUNTING AND MES INTEGRATION AND IMPLEMENTATION CYCLES); SECOND STEP: COMPARISON OF THE FUNCTIONAL DIFFERENCES BETWEEN THE THREE CORE NEEDS OF EACH PRODUCT (E.G., COST ACCOUNTING: WE SUPPORT BATCH ACCOUNTING AND THE MONTHLY ACCOUNTING OF THE BUTTERFLIES); THIRD STEP: REFERENCE TO Q3 CUSTOMER FEEDBACK (COMPLEMENTARY SUMMARY OF CUSTOMER FEEDBACK) TO VERIFY THE PRACTICAL IMPACT OF FUNCTIONAL DIFFERENCES; FOURTH STEP: SUMMARY OF COMPETITIVE ADVANTAGES AND IDENTIFICATION OF FIT SCENARIOS FOR MY PRODUCT\u3002<\/p>\n<p>Final output: presented according to the table \" Demand dimension -- our function -- butterfly function -- client feedback -- competitive advantage \" \u3002<\/p>\n<p><em><strong>Strategy 2: Distinction of tasks - Dismantling complex tasks into children<\/strong><\/em><\/p>\n<p><strong>Core logic:<\/strong>COMPLEX TASKS OFTEN INVOLVE \u201cINFORMATION GATHERING AND ANALYSIS OF OUTPUTS OPTIMIZED\u201d, AFTER WHICH EACH LINK FOCUSES ON A SINGLE OBJECTIVE, AND AI OUTPUTS ARE OF HIGHER QUALITY AND ALLOW USERS TO CONTROL THE RESULTS OF EACH LINK\u3002<\/p>\n<ul>\n<li><strong>Disaggregation principle:<\/strong>Disaggregation by business process progression (e.g., white paper: Data collection and analysis of pain and pain design framework to optimize language)<\/li>\n<li><strong>Association logic:<\/strong>Clear linkages between sub-missions (e.g., the second step pain point analysis based on first step industry data)<\/li>\n<li><strong>Step-wise optimization:<\/strong>Optimizing Prompt to move to the next step after the result of each sub-task to avoid an accumulation of errors\u3002<\/li>\n<\/ul>\n<p><strong>Case in point:<\/strong>Preparation of a White Paper on the Digital Transformation of Small and Medium-sized Manufacturing Enterprises in 2025 (3,000 words)<\/p>\n<p>Sub-Task 1 (data collection): collection of digital transformation data from 2024-2025 for small and medium-sized manufacturing enterprises, with emphasis on digital penetration, core input orientation, key factors for success in transformation, labelling of data sources (IDC\/McK) and output of Markdown tables<\/p>\n<p>Sub-mission 2 (analytical pain points): based on sub-mission 1 industry data, analysis of Top3 pain points for the digital transformation of small and medium-sized manufacturing enterprises, with data support for each pain point (e.g. 30% enterprises providing feedback on inefficient production schedules), list of output points<\/p>\n<p>Sub-Target 3 (design framework): Based on the pain analysis of sub-task 2, design the White Paper framework, which contains section 5 on \u201cThe state of the industry \u2014 core pain points \u2014 transformation pathways \u2014 case references \u2014 selection recommendations\u201d, each of which sets out the core elements<\/p>\n<p>Sub-Task 4 (filling): prepare the full text of the White Paper in the framework of Sub-Task 3 with a language style professional and common, and the case highlights in part the application of our product (which needs to be supplemented by some specific information on your company ' s products) and output the Markdown format<\/p>\n<p>Sub-mission 5 (optimal language): Optimization of the full text of sub-mission 4, correction of grammatical errors, adjustment of the paragraph logic to ensure fluidity and output of the final version\u3002<\/p>\n<p><em><strong>STRATEGY 3: ROLE EMPOWERMENT - PUT AI IN A PROFESSIONAL PERSPECTIVE<\/strong><\/em><\/p>\n<p><strong>Core logic:<\/strong>SET AI \u201cSPECIFIC ROLE + INDUSTRY EXPERIENCE + CORE COMPETENCIES\u201d TO GUIDE IT IN THINKING FROM A PROFESSIONAL POINT OF VIEW AND TO OUTPUT MORE RELEVANT CONTENT. ROLE-SETTING REQUIRES \u201cPRECISION RATHER THAN GENERALIZATION\u201d AND AVOID VAGUE EXPRESSIONS SUCH AS \u201cTHE WORLD'S TOP EXPERT\u201d\u3002<\/p>\n<ul>\n<li><strong>Role composition:<\/strong>INDUSTRY BACKGROUND (E.G. FIVE-YEAR MANUFACTURING MES SALES EXPERIENCE) + PROFESSIONAL COMPETENCIES (E.G. FAMILIARIZATION WITH THE AUTO INDUSTRY PRODUCTION PROCESS) + CORE PERSPECTIVES (E.G. FOCUS ON CLIENTS ROI)<\/li>\n<li><strong>Perspective guide:<\/strong>(a) Clarifying the core focus of the role (e.g. the Director of Production is concerned with efficiency and quality, the Director of Finance is concerned with cost and compliance)<\/li>\n<li><strong>Avoid over-set:<\/strong>No unrelated capacity is stacked (e.g., preparation of a financial programme does not require a \"proficient programming\" requirement)\u3002<\/li>\n<\/ul>\n<p><strong>The error demonstration:<\/strong>ASSUMING YOU'RE AN EXPERT IN THE INDUSTRY, WRITE AN INTRODUCTION TO THE MES SYSTEM\u3002<\/p>\n<p><strong>Error point:<\/strong>Role blurry, output broad\u3002<\/p>\n<p><strong>The correct demonstration:<\/strong>NOW THAT YOU ARE A CONSULTANT WITH FIVE YEARS OF PRE-SALE EXPERIENCE IN THE AUTOMOBILE INDUSTRY, WHO IS FAMILIAR WITH NEW ENERGY VEHICLE PRODUCTION PROCESSES, PLEASE WRITE TO THE DIRECTOR-GENERAL OF AUTOMOBILE PRODUCTION AN INTRODUCTION TO THE MES SYSTEM, FOCUSING ON \u201cIMPROVING PRODUCTION EFFICIENCY, RETROACTIVITY OF QUALITY, OPTIMIZATION OF EQUIPMENT USE\u201d FROM THREE ANGLES, WITH LANGUAGE STYLES THAT REPLACE VAGUE EXPRESSION WITH QUANTITATIVE DATA\u3002<\/p>\n<p><em><strong>Policy 4: Pre-fill responses - Forced output structured format<\/strong><\/em><\/p>\n<p><strong>Core logic:<\/strong>FOR TASKS REQUIRING A FIXED FORMAT (E.G. JSON DATA, FORMS, DEMAND LIST), PREFILL A PARTIAL CONTENT FRAMEWORK THAT ALLOWS AI TO FILL IN KEY INFORMATION DIRECTLY, AVOID REDUNDANT REPRESENTATIONS AND CAN BE IMPORTED DIRECTLY INTO THE SYSTEM\u3002<\/p>\n<ul>\n<li><strong>Format frame:<\/strong>DESIGN ON A PHYSICAL BASIS (E.G. JSON FOR SYSTEM IMPORT, TABLES FOR DOCUMENT COLLABORATION)<\/li>\n<li><strong>Field definition:<\/strong>IDENTIFY THE FORMAT REQUIREMENTS FOR EACH FIELD (E.G., PRIORITY: HIGH\/MEDIUM\/LOW, MODULE NAME: MES - PRODUCTION SCHEDULE)<\/li>\n<li><strong>Remove redundancy:<\/strong>Specifically require only output of filled format without any mattress or explanation\u3002<\/li>\n<\/ul>\n<p><strong>Case in point:<\/strong>DRAWING DEMAND FROM CLIENT FEEDBACK AND GENERATING DEMAND LISTS IN JSON FORMAT (FOR THE PRODUCT DEPARTMENT DEMAND MANAGEMENT SYSTEM)<\/p>\n<p>Prompt: Draw core needs from the following client feedback, fill them in the pre-filled JSON format, and only export JSON without any mattress. Client feedback: We are e-manufacturing enterprises, using your MES system, to export only Excel, hoping to support PDF formats; moreover, equipment alarms can only be alerted in the system, hoping to be synchronized to company micro-mail\u3002<\/p>\n<p>ENTER JSON:<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-48132\" title=\"de2e0e1dp00t84j0b0031d000u3fm\" src=\"https:\/\/www.1ai.net\/wp-content\/uploads\/2025\/12\/de2e0e1dp00t84j0b0031d000u0003fm.png\" alt=\"de2e0e1dp00t84j0b0031d000u3fm\" width=\"1080\" height=\"123\" \/><\/p>\n<p>OUTPUT JSON:<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-48133\" title=\"a414b395j00t84j0b005td000u04em\" src=\"https:\/\/www.1ai.net\/wp-content\/uploads\/2025\/12\/a414b395j00t84j0b005td000u0004em.jpg\" alt=\"a414b395j00t84j0b005td000u04em\" width=\"1080\" height=\"158\" \/><\/p>\n<p><em><strong>Strategy 5: Uncertainty Management - Enhancing export credibility<\/strong><\/em><\/p>\n<p><strong>Core logic:<\/strong>AI IS EXPLICITLY INFORMED THAT IT \u201cDOES NOT KNOW ABOUT, DOES NOT FABRICATE INFORMATION\u201d ESPECIALLY FOR DATA CLASSES, DE FACTO TASKS (E.G., INDUSTRY REPORTS, COMPETITION ANALYSIS) AND AVOIDS THE GENERATION OF FALSE DATA OR PARTIAL FINDINGS BY AI TO ENHANCE THE CREDIBILITY OF THE OUTPUT\u3002<\/p>\n<ul>\n<li><strong>Rule:<\/strong>(a) Clarify the manner in which \u201cuncertain information\u201d is labelled (e.g., for additional research, for insufficient data and for further validation)<\/li>\n<li><strong>Data constraints:<\/strong>Emphasizing that \u201cthe generation of content based on the information provided does not create unreferenced data\/cases\u201d<\/li>\n<li><strong>Distinction recommendations:<\/strong>Clear \u201cdata-supported recommendations\u201d and \u201cvalid recommendations\u201d to avoid misleading decision-making\u3002<\/li>\n<\/ul>\n<p><strong>Case in point:<\/strong>ANALYSIS OF CAUSES OF Q3 CLIENT LOSS<\/p>\n<p>Prompt: Analysis of the causes of the loss of Q3 CRM products, requesting:<\/p>\n<ol>\n<li>Based on data provided (32 lost clients: 18 feedback services were slow to respond, 8 were overpriced and 6 were not explained)<\/li>\n<li>(a) The form is organized by \u201ccause of loss - number of clients - percentage\u201d<\/li>\n<li>Six families that did not state their reasons were identified as \u201cto be supplemented\u201d and did not speculate about the specific reasons<\/li>\n<li>Part of the proposal distinguishes between \u201cdata-supported\u201d (e.g., optimization of service response processes) and \u201cvalidation required\u201d (e.g., research does not indicate the real needs of clients for reasons)\u3002<\/li>\n<\/ol>\n<p><strong>3.<\/strong><strong>High-level skills<\/strong><\/p>\n<p>For super-complex tasks (e.g., multi-modular content generation, cross-cutting programme design, professional mapping), more innovative Prompt techniques need to be used to break conventional output limits, taking into account AI ' s capacity characteristics\u3002<\/p>\n<p><em><strong>Skills 1: Cross-mode-linking - consolidate text, chart, data<\/strong><\/em><\/p>\n<p><strong>Core logic:<\/strong>LLM not only produces text, but also charts by specific syntax (e.g. Mermaid, LaTeX) that combine text and chart to make the output more persuasive\u3002<\/p>\n<ul>\n<li><strong>Chart Generation:<\/strong>Generate flow charts, architecture charts, time-series charts (e.g., product module flow charts, business process diagrams) in Mermaid syntax<\/li>\n<li><strong>Data visualization:<\/strong>Comparative data are presented in tables and hierarchical relationships in lists<\/li>\n<li><strong>Cross-state collaboration:<\/strong>The text part explains what the chart means, the chart part supports the text view and forms an integrated output of the text + chart\u3002<\/li>\n<\/ul>\n<p><strong>Case in point:<\/strong>DESIGN FUNCTIONAL ARCHITECTURE FOR ERP PRODUCTS<\/p>\n<p>Prompt: Designing an ERP product functional framework for small and medium-sized manufacturing enterprises that requires:<\/p>\n<ol>\n<li>Text section: Sub-finance module, production module, supply chain module, human resources module, describing core functions and solutions<\/li>\n<li>Chart section: Generate functional architecture in Mermaid syntax to demonstrate the relationship between modules<\/li>\n<li>DATA SUPPORT: THE QUANTITATIVE EFFECT OF REDUCING XX COSTS AND INCREASING XX EFFICIENCY IS INDICATED FOR EACH MODULE\u3002<\/li>\n<\/ol>\n<p><em><strong>Skills 2: Knowledge Injection in the Field - Supplementary Information in the Professional Field<\/strong><\/em><\/p>\n<p><strong>Core logic:<\/strong>For highly specialized areas (e.g., medical, legal, industrial), LLM pre-training knowledge may be lagging or insufficient, requiring the injection of field knowledge (e.g., industry standards, business processes, professional terms) in Prompt to enhance the professionalism of output\u3002<\/p>\n<ul>\n<li><strong>Type of knowledge:<\/strong>(A) INDUSTRY STANDARDS (E.G. COMPLIANCE WITH THE GSP, COMPLIANCE WITH ISO 9001), BUSINESS PROCESSES (E.G., BACKTRACKING OF PHARMACEUTICALS IN PHARMACEUTICAL ENTERPRISES, ETC.), PROFESSIONAL TERMS (E.G., BATCH RETROACTIVE, COMPLIANCE STATEMENTS, ETC.)<\/li>\n<li><strong>Injection mode:<\/strong>Briefly describe in the background section, avoid stacking and focus on mandate-related knowledge<\/li>\n<li><strong>Validation logic:<\/strong>AI IS REQUIRED TO QUOTE THE INFUSION OF KNOWLEDGE IN THE OUTPUT TO ENSURE PROFESSIONALISM\u3002<\/li>\n<\/ul>\n<p><strong>Case in point:<\/strong>PREPARATION OF AN ERP PRODUCTS PROGRAMME FOR PHARMACEUTICAL ENTERPRISES<\/p>\n<p>Prompt: Preparation of an ERP product programme for pharmaceutical enterprises<\/p>\n<p>COMPLIANCE REQUIREMENTS: COMPLIANCE WITH THE GSP AND SUPPORT FOR A FULL LIFE CYCLE OF THE DRUG<\/p>\n<p>(c) Core pain points: batch backtracking, complex reporting of compliance, slow inventory turnaround\u3002<\/p>\n<p>REQUEST: THE PROGRAMME HIGHLIGHTS HOW TO MEET THE GSP NORMS, ADDRESSES CORE PAIN POINTS, AND REFERS TO SPECIALIZED TERMS SUCH AS DRUG TRACEABILITY, COMPLIANCE STATEMENTS, ATTACHED TO THREE MEDICAL ENTERPRISE CLIENT CASES\u3002<\/p>\n<p><em><strong>SKILLS 3: REMITTANCE LOOP EMBEDDED - OPTIMIZING AI<\/strong><\/em><\/p>\n<p><strong>Core logic:<\/strong>Embedding a \u201cself-checking\u201d link in Prompt to allow AI to generate output, self-assessed and optimized according to pre-set criteria, and to reduce the number of artificial overlaps\u3002<\/p>\n<ul>\n<li><strong>Inspection criteria:<\/strong>Task-based targeting (e.g., whether core functions are highlighted, format requirements are met, data support is included)<\/li>\n<li><strong>Optimizing command:<\/strong>Clarity on how to modify \u201cif they do not meet the criteria\u201d (e.g., by adding one relevant customer case if no case is included)<\/li>\n<li><strong>Number of cycles:<\/strong>Sets a cycle of one to two and avoids excessive overlaps leading to redundancy of content\u3002<\/li>\n<\/ul>\n<p><strong>Case in point:<\/strong>Writing HR SaaS Product Promotion Program<\/p>\n<p>Prompt: Preparation of HR SaaS product promotions for the manufacturing of HRD. Core requirements:<\/p>\n<ol>\n<li>(a) Highlighting of attendance and production scheduling<\/li>\n<li>Including 1 client case<\/li>\n<li>400 words<\/li>\n<li>End with a free trial link\u3002<\/li>\n<\/ol>\n<p>Self-censorship and optimization: Once a case has been produced, check whether the four above-mentioned requirements have been met, if not met, with targeted modifications (in the absence of a case, supplemented by a reduced number of words), and ultimately output the optimized text\u3002<\/p>\n<p><strong>Site site: Prompt field template for four core operations<\/strong><\/p>\n<p>In order to put the above-mentioned approach to a specific business scene, I have assembled a field template for the four core scenarios of the To B industry, \u201cContent creation, data analysis, programme planning, customer service\u201d, which can be used either directly or adjusted\u3002<\/p>\n<p><strong>Scenario 1: Content creation \u2014 industry white paper<\/strong><\/p>\n<p>Template core logic: Data support + pain point analysis + case validation + selection recommendations\u3002<\/p>\n<p>Mission: preparation of White Paper on Selection of Small and Medium Enterprises [Themes] in 2025 [industry] (3,000 words)<\/p>\n<p>1. Basic information:<\/p>\n<p>\u2013 TARGET AUDIENCE: [INDUSTRY] SMALL AND MEDIUM-SIZED ENTERPRISES [DECISION-MAKING ROLES SUCH AS IT DIRECTORS\/FINANCE DIRECTORS]<\/p>\n<p>\u2013 Core objective: to help target audiences to identify selection criteria and highlight our product strengths<\/p>\n<p>\u2013 Style requirements: professional and practical, including data support and client cases, and avoiding a multiplicity of technical terms\u3002<\/p>\n<p>2. Mind chain steps:<\/p>\n<p>ANALYSIS OF THE CURRENT STATE OF THE INDUSTRY: THE 2024 REPORT OF THE [AUTHORITY BODY, SUCH AS IDC\/MACKENZIE] PRESENTS THE STATUS OF THE DIGITAL TRANSFORMATION OF THE [INDUSTRY] (E.G. PENETRATION RATE, INPUT SIZE) WITH DATA TABLES<\/p>\n<p>2 SELECT PAIN POINT DISASSEMBLY: ANALYSIS OF THE CORE PAIN POINT OF THE [INDUSTRY] SMALL AND MEDIUM-SIZED ENTERPRISE SELECTION BY 4 DIMENSIONS OF \u201cFUNCTIONAL MATCHING, INTEGRATION CAPACITY, IMPLEMENTATION CYCLE, COST INPUT\u201d WITH DATA SUPPORT FOR EACH PAIN POINT (E.G. \u201c60% ENTERPRISE FEEDBACK `INTEGRATION DIFFICULTY'\u201d)<\/p>\n<p>THREE SELECTION CRITERIA: FOR PAIN POINTS, FOUR CORE SELECTION CRITERIA FOR \"FUNCTIONAL SUITABILITY, TECHNOLOGICAL MATURITY, SPEED OF SERVICE RESPONSE, ROI\" ARE PROPOSED, EACH WITH SPECIFIC MEASURES (E.G. \"ROI: ONE YEAR BACK\")<\/p>\n<p>4 Comparative analysis of competitions: highlighting our strengths (e.g., \u201cWe have a three-month implementation cycle and six-month competitions\u201d) over differences in the selection criteria between our products and [competition 1\/competition 2]<\/p>\n<p>5 Client case validation: 3 [industry] customer selection cases, presented as \u201ccustomer background - selected pain points - solutions - use effects\u201d, highlighting the actual value of our product<\/p>\n<p>Six alternative hands-on advice: four phases of \u201cdemand combing - product testing - implementation planning - impact assessment\u201d with concrete operational steps\u3002<\/p>\n<p>3. Output format:<\/p>\n<p>\u2013 Markdown format, which contains the \u201ctitle - Catalogue - Text (Part 6) - Final (with entry for the probationary application)\u201d<\/p>\n<p>\u2013 Each part of the text is summed up with \u201c[core view]\u201d at the beginning, and key data is thicker<\/p>\n<p>\u2013 Cases are presented in tables in part and competitions in tables\u3002<\/p>\n<p>4. Attention:<\/p>\n<p>\u2013 Uncertain data (e.g. the latest price of the bid) are labelled \u201cto be updated\u201d and are not made up<\/p>\n<p>\u2013 Avoid undermining the competition and focus on \u201cHow our products work out the pain points for their clients\u201d\u3002<\/p>\n<p><strong>Scenario 2: Data analysis \u2014 sales data redisposal report<\/strong><\/p>\n<p>Template Core Logic: Data Dismantling + Reason Analysis + Strategy Proposal + Target Adjustment\u3002<\/p>\n<p>\u2022 Mandate: to generate a sales duplicate report based on [time period] [product] sales data<\/p>\n<p>1. Data background:<\/p>\n<p>\u2013 DATA RANGE: [TIME PERIOD] [REGIONAL\/TEAM] SALES DATA, CORE INDICATORS: NUMBER OF NEW CLIENTS (TARGET X, ACTUAL X), UNIT RATE (TARGET X%, ACTUAL X%), UNIT PRICE (TARGET X0.000, ACTUAL X0.000), NUMBER OF LOST CUSTOMERS (X HOUSEHOLDS)<\/p>\n<p>- Use: Rewinding [the decision-making role, such as the sales director] for [the next cycle] adjustments to the sales strategy\u3002<\/p>\n<p>2. Analysis logic<a href=\"https:\/\/www.1ai.net\/en\/tag\/%e6%8f%90%e7%a4%ba%e8%af%8d\" title=\"[View articles tagged with [cue word]]\" target=\"_blank\" >Prompt word<\/a>Serial:<\/p>\n<p>1 DISMANTLING OF DATA: DISMANTLING OF CORE INDICATORS BY \u201cREGIONAL, SALES LEVEL, CUSTOMER INDUSTRY\u201d TO IDENTIFY THE MOST PERFORMING\/LOWEST DISAGGREGATED DIMENSIONS (E.G. \u201c35% IN EAST CHINA, 20% IN NORTH CHINA\u201d), PRESENTED IN TABLES<\/p>\n<p>2 GAP ANALYSIS: ANALYSIS OF THE CAUSES IN RELATION TO SPECIFIC CASES FROM THE PERSPECTIVE OF \u201cCUSTOMS DEMAND MATCHING, MARKETING CAPACITY, MARKET COMPETITION, EXTERNAL ENVIRONMENT\u201d (E.G. \u201cCUSTOMS SINGLE RATE 15%, BELOW 35% FOR OLDER PERSONS, DUE TO LACK OF KNOWLEDGE OF PRODUCTS\u201d) FOR THE INDICATOR OF UNDERACHIEVEMENT (E.G. SINGLE RATE)<\/p>\n<p>3 LOSS ANALYSIS: ANALYSIS OF LOST CUSTOMERS BY \u201cINDUSTRY, CAUSE OF LOSS, POSSIBILITY OF RECOVERY\u201d, LABELLING OF \u201cPOSSIBLE CUSTOMERS\u201d AND RECOVERY STRATEGIES<\/p>\n<p>4 Strategy recommendations: based on the results of the analysis, concrete land-defeating measures are given in four areas: \u201cresource-sorting, staff training, customer maintenance, market outreach\u201d (e.g. \u201cIn October, new-person product training is conducted once a week, with instruction in Top sales\u201d)<\/p>\n<p>5 TARGET ALIGNMENT: RECOMMENDATIONS FOR ADJUSTMENTS TO THE CORE INDICATORS [FOR THE NEXT CYCLE] ARE MADE IN THE CONTEXT OF THE RESULTS OF THE EXERCISE (E.G. \u201cTHE NUMBER OF ADDITIONAL CLIENTS IS ADJUSTED TO X, THE UNIT RATIO GOAL X%\u201d)\u3002<\/p>\n<p>3. Output format:<\/p>\n<p>\u2013 Markdown table for data dismantling, loss analysis<\/p>\n<p>\u2013 Gap analysis, strategy proposal with a \u201cproblem point - concrete performance - solution\u201d structure<\/p>\n<p>- At the end, add \u201cthe core objectives of the [next cycle] and the timetable for implementation\u201d\u3002<\/p>\n<p>4. Attention:<\/p>\n<p>\u2013 Accurate calculation of the data (e.g. unit = unit\/business) and clarification of the formula when it is not clear<\/p>\n<p>\u2013 It is suggested that the section should clarify \u201cresponsibles, time nodes, measures\u201d and avoid generalities\u3002<\/p>\n<p><strong>Scenario 3: Programme Planning - Annual Market Extension Programme<\/strong><\/p>\n<p>Template core logic: Target dismantling + channel strategy + budget allocation + implementation monitoring + risk response\u3002<\/p>\n<p>Mission: development of [product] annual market extension programme for 2025<\/p>\n<p>1. Operational context:<\/p>\n<p>- PRODUCT POSITIONING: [PRODUCT TYPE, E.G. MES], CORE ADVANTAGES: [E.G. \u201cIMPROVING PRODUCTION EFFICIENCY\u201d \u201cQUALITY RETROACTIVE\u201d), TARGET CLIENTS: [INDUSTRY + SIZE, E.G. \u201cSMALL AND MEDIUM MANUFACTURING ENTERPRISES]<\/p>\n<p>\u2013 STATUS 2024: CLIENT X, MARKET OCCUPANCY X%, MAIN ACCESS: [E.G. INDUSTRY FAIR\/OLD CUSTOMER RECOMMENDATION], COST X\/HOME<\/p>\n<p>\u2013 TARGET 2025: NEW CUSTOMERS X (X% FOR [INDUSTRY BREAKDOWN, E.G., MACHINERY\/FOOD\/MEDICINE]) WITH MARKET SHARE RISING TO X% AND REDUCED COST TO CUSTOMERS X%\u3002<\/p>\n<p>Programme framework:<\/p>\n<p>TARGET 1 DISMANTLING: DISMANTLING THE ANNUAL TARGET BY \u201cQUARTER, INDUSTRY, CHANNEL\u201d, FOR EXAMPLE: Q1 ADDS X (MECHANIC X, FOOD X) AND RECEIVES A COST OF X DOLLARS\/HOME<\/p>\n<p>Channel 2 strategy:<\/p>\n<p>\u2013 underline channels: a list of key participants in 2025 (e.g., the shanghai industrial fair in march), with a clear budget for each event, targets for the recipients, and design priorities for booth<\/p>\n<p>\u2013 Online channels: content marketing (one white paper, two cases per month, five short videos), distribution channels (industry media\/micro-publics\/diversities), core themes (e.g. \u201cPerformance efficiency cases\u201d)<\/p>\n<p>\u2013 OLD CLIENT RECOMMENDATION: DESIGN OF A RECOMMENDED INCENTIVE MECHANISM (E.G., A THREE-MONTH FREE SERVICE RECOMMENDED BY AN OLD CLIENT), WITH TARGET RECOMMENDATION RATIO X%<\/p>\n<p>BUDGET ALLOCATION: THE TOTAL BUDGET OF 100,000, ALLOCATED ACCORDING TO CHANNEL (X0.000 BELOW LINE\/X0.000 ON LINE\/X0.000 RECOMMENDED BY OLDER CLIENTS) + X0.000 EMERGENCY, WITH A QUARTERLY BUDGET SPLIT TABLE<\/p>\n<p>4 Implementation monitoring: development of a monthly watch sheet (core indicators: number of visitors, cost of clients, rate of channel conversion) identifying those responsible (e.g., \u201cpublishing of content\u201d)<\/p>\n<p>5 Risk response: Prejudicing possible risks (e.g., poor performance of fairs, changes in industry needs) and providing responses (e.g., increasing on-line access, adjusting programme focus)\u3002<\/p>\n<p>3. Output format:<\/p>\n<p>\u2013 PPT OUTLINE FORMAT, EACH PART OF WHICH CONTAINS \u201cTITLE-CORE CONTENT-RESPONSIBLE-TIME NODES\u201d<\/p>\n<p>\u2013 Key data presented in tables (budget distribution table, quarterly target dismantling table)<\/p>\n<p>\u2013 THE FEASIBILITY OF SUPPORTING STRATEGIES, CITING SUCCESS STORIES FROM 2024 (E.G., \u201c25 AT THE SHANGHAI INDUSTRIAL FAIR IN 2024, AT A COST OF X DOLLARS\/HOME\u201d)\u3002<\/p>\n<p><strong>SCENARIO 4: CLIENT SERVICES - PRODUCT FAQ<\/strong><\/p>\n<p>Template core logic: HF filter + structured answer + common expression + operational guidance\u3002<\/p>\n<p>MISSION: WRITING [PRODUCTS] CLIENT FAQ (FOR [USER ROLE, E.G. HR COMMISSIONER\/PRODUCER])]<\/p>\n<p>Background information:<\/p>\n<p>- Target users: [user roles] using [products], non-technical background<\/p>\n<p>\u2013 Core requirements: to address HF issues in day-to-day operations and to reduce the number of passenger service consultations<\/p>\n<p>\u2013 Style requirements: language is common, steps are clear, and each issue is accompanied by \u201cOperational guidelines\u201d (word describes the key steps of the screenshot)\u3002<\/p>\n<p>2. Content requirements:<\/p>\n<p>1 Question screening: select Top10 HF based on [time] passenger data (e.g., \u201cHow do you export monthly reports?\u201d \u201cHow to modify employee information?\u201d)<\/p>\n<p>2 Structure of answer: each question is written by \"Question - Reason - Solutions (Step) - Attention\", Example:<\/p>\n<p>How do you export monthly attendance reports<\/p>\n<p>Question: Need to export attendance data to financial accounting salaries<\/p>\n<p>Reason: System default report format is Excel and manual export is required<\/p>\n<p>Solutions: 1. Log-in system, click on left \" time and attendance management \" ; 2. Select \" monthly statistics \" , set statistical months; 3. Click \" Export \" , select Excel format, to the top right; 4. Wait 30 seconds for download<\/p>\n<p>ATTENTION: 1 REQUIRES CONFIRMATION THAT DATA HAVE BEEN CLEARED PRIOR TO EXPORT; 2 EXPORT FAILURE CAN CHECK THE NETWORK OR CONTACT THE PASSENGER SERVICE (TELEPHONE XXX)<\/p>\n<p>3 Classification: classified as \" Functional modules (e.g., attendance management\/staff management\/report export) \" , each module is sorted in question order\u3002<\/p>\n<p>3. Output format:<\/p>\n<p>\u2013 Markdown format, with ## and ##<\/p>\n<p>\u2013 Steps are listed in an orderly manner, and attention is given to matters beginning with \u201cNote:\u201d<\/p>\n<p>\u2013 At the end, add \u201cother problem feedback channels (guest calls\/business tweets)\u201d\u3002<\/p>\n<p>4. Attention:<\/p>\n<p>\u2013 AVOID TECHNICAL TERMS (E.G. \u201cAPI INTERFACE\u201d AND \u201cSYSTEM INTERFACE\u201d)<\/p>\n<p>\u2013 Different scenarios (e.g. \u201cStaff Separations Active\/Preventive\u201d) need to be described separately\u3002<\/p>\n<p><strong>Five pits that Prompt designed<\/strong><\/p>\n<p>Even if the above methods are available, in practice they may result in the failure of AI output to meet the standards by negligence in detail. Here are six common mistakes and their corresponding solutions to help you avoid pits and enhance Prompt's effectiveness\u3002<\/p>\n<p><strong>Error 1: Overdesigned<\/strong><\/p>\n<p><strong>Prompt is long and complex, with vague core needs<\/strong><\/p>\n<p><strong>Performance:<\/strong>For the purpose of writing a PRD, a product manager has added to Prompt the expression \u201cwith 10 years of To B product experience, master of PRD writing specifications, familiar with ISO 9001, IEE standards ...\u201d, Prompt has 500 words and the content of AI output is terminological and lacks real demand\u3002<\/p>\n<p><strong>reason:<\/strong>OVERSTRETCHING OF ROLES, NORMS, CONTEXTS, WHICH MAKES IT IMPOSSIBLE FOR AI TO IDENTIFY CORE NEEDS\u3002<\/p>\n<p><strong>Solutions:<\/strong><\/p>\n<ul>\n<li><strong>Core requirements ahead:<\/strong>THE BACKGROUND IS SUPPLEMENTED BY A DIRECT INTRODUCTION TO \u201cWRITING XX MODULE PRD\u201d<\/li>\n<li><strong>Role-setting precision<\/strong>INSTEAD OF THE WORDS \u201cEXACT IN 10 INDUSTRIES\u201d, REPLACE, FOR EXAMPLE, \u201cPRODUCT MANAGERS FAMILIAR WITH THE NEEDS OF MS IN THE FOOD INDUSTRY\u201d<\/li>\n<li><strong>As required:<\/strong>ONLY REFERENCES TO MISSION-RELATED NORMS (E.G. PRD NEEDS TO INCLUDE FUNCTIONAL DESCRIPTIONS, INPUT OUTPUT, ABNORMAL HANDLING)\u3002<\/li>\n<\/ul>\n<p><strong>Error 2: Base is missing<\/strong><\/p>\n<p><strong>Relying on advanced skills, ignoring core information<\/strong><\/p>\n<p><strong>Performance:<\/strong>A MARKETING MANAGER USES A \u201cTHINK CHAIN\u201d TO WRITE AN OUTREACH PROGRAMME WITH A DETAILED DESIGN OF THE STEPS, BUT DOES NOT STATE \u201cTARGETING CUSTOMERS, OUTREACH OBJECTIVES\u201d, AND THE AI-OUTPUT PROGRAMME HAS A COMPLETE LOGIC BUT DOES NOT FIT THE BUSINESS\u3002<\/p>\n<p><strong>reason:<\/strong>Core instructions are vague and advanced skills do not compensate for the lack of basic information\u3002<\/p>\n<p><strong>Solutions:<\/strong><\/p>\n<ul>\n<li>Prompt looks at the three core elements: Are the objectives clear? Is the context clear? Are the constraints specific<\/li>\n<li>(a) Complex tasks are first \u201cwritten with basic skills\u201d and then optimized with advanced skills<\/li>\n<li>If the output deviates, the basic information is supplemented and the advanced skills adjusted\u3002<\/li>\n<\/ul>\n<p><strong>ERROR 3: UNDEFINED HIDDEN NEEDS, FREE AI<\/strong><\/p>\n<p><strong>Performance:<\/strong>A sale allowed AI \u201cto write a follow-up e-mail to a potential customer\u201d and did not state that \u201cthe customer is the manufacturing owner, who previously communicated with the product demo, focusing on ROI\u201d, and that the mail sent out by AI was generic and did not respond\u3002<\/p>\n<p><strong>reason:<\/strong>IMPLICIT NEEDS (E.G., CUSTOMER FOCUS, COMMUNICATION HISTORY) ARE NOT CLEAR AND AI CANNOT BE ACCURATELY MATCHED\u3002<\/p>\n<p><strong>Solutions:<\/strong><\/p>\n<ul>\n<li><strong>Create NeedChecklist:<\/strong>Ensuring that customer roles, trades, pain points, communication history are included<\/li>\n<li><strong>We're going to use the \"counter-examination\" method:<\/strong>IF YOU WERE AI, WHAT WERE THE QUESTIONS? FOCUS ON WHAT<\/li>\n<li><strong>Identify hidden needs:<\/strong>For example, clients are concerned about long implementation cycles and need to emphasize \u201cthree months of implementation\u201d in their mail\u3002<\/li>\n<\/ul>\n<p><strong>Error 4: Skills stacking<\/strong><\/p>\n<p><strong>If you need it or not, use all of your advanced skills<\/strong><\/p>\n<p><strong>Performance:<\/strong>A pre-sale consultant writes a simple client case, using the term \u201cthink chain, task split, role set\u201d, which takes one hour, and the result is not much different from the \" Example Guide.\"\u3002<\/p>\n<p><strong>reason:<\/strong>Skills choices are less efficient than \u201cdemand-based matching\u201d over-complex approaches\u3002<\/p>\n<p><strong>Solutions:<\/strong><\/p>\n<ul>\n<li><strong>Selecting skills by complexity of need:<\/strong>Simple tasks (e.g., case writing, data collation) use basic techniques; complex tasks (e.g., white papers, competition analysis) use progressive strategies<\/li>\n<li><strong>Guided by the \u201cminimum cost principle\u201d:<\/strong>It can be solved with one technique, not many\u3002<\/li>\n<\/ul>\n<p><strong>Error 5: Neglecting ethics<\/strong><\/p>\n<p><strong>Prompt contains sensitive information or inappropriate guidance<\/strong><\/p>\n<p><strong>Performance:<\/strong>A client service allows AI to analyse client feedback, and Prompt directly carries sensitive information such as a client ' s clear name, telephone, address, and risks privacy disclosure\u3002<\/p>\n<p><strong>reason:<\/strong>Failure to consider data privacy and ethical norms leads to compliance risks\u3002<\/p>\n<p><strong>Solutions:<\/strong><\/p>\n<ul>\n<li><strong>Data desensitization:<\/strong>REPLACE PERSONAL INFORMATION WITH ANONYMOUS ID (E.G. CLIENT 001)<\/li>\n<li><strong>Follow the data minimization principle:<\/strong>(b) Provide only the minimum information necessary to fulfil its mandate<\/li>\n<li><strong>Avoid sensitive orientation:<\/strong>Prompt, for example, does not design \u201chow to devalue competition\u201d, \u201chow to mislead customers\u201d\u3002<\/li>\n<\/ul>\n<p><strong>Some reflections on enterprise-level Prompt capacity-building<\/strong><\/p>\n<p>For enterprises, Prompt capabilities are not only personal skills, but also the core competitiveness of organizational efficiency, and many of them now go to AI \u201cfamily buckets\u201d to enhance the productivity of their internal staff, so that the scale-up and efficient application of AI tools can be achieved by creating a systematic organizational empowerment system that allows team members to quickly master the Prompt approach\u3002<\/p>\n<p><strong>Create'Prompt Template Library<\/strong><\/p>\n<p>\/ Lower use threshold<\/p>\n<ul>\n<li><strong>Classification dimensions:<\/strong>Classification by business scene (content\/analysis\/programme\/service), product line, output format<\/li>\n<li><strong>Template content:<\/strong>Each template contains \"Prompt originals, use scenes, optimization techniques, impact cases\"<\/li>\n<li><strong>Maintenance mechanisms:<\/strong>The appointment of a dedicated person to update on a monthly basis to collect quality team cases to be added to the library<\/li>\n<li><strong>Tool Recommendation:<\/strong>A NUMBER OF ONLINE FILES AND KNOWLEDGE MANAGEMENT TOOLS ARE STORED TO SUPPORT KEYWORD SEARCHES (E.G., THE ERP CLIENT CASE)\u3002<\/li>\n<\/ul>\n<p><strong>Conduct of a training competition on \u201cPrompt dismantling\u201d<\/strong><\/p>\n<p>\/ Upgrade structured thinking<\/p>\n<ul>\n<li><strong>Question:<\/strong>Selecting complex tasks in the near future (e.g., the Market Extension Programme 2025)<\/li>\n<li><strong>Dismantling:<\/strong>Grouping to decomposition tasks using \u201ctask split\u201d and design Prompt<\/li>\n<li><strong>Practice:<\/strong>Each group produces AI output using Prompt<\/li>\n<li><strong>Rewind:<\/strong>Comparing the outputs of the groups and summarizing the commonality of excellent Prompt<\/li>\n<li><strong>Frequency:<\/strong>Quarterly 1-2 meetings to avoid theoreticalization in the context of actual tasks\u3002<\/li>\n<\/ul>\n<p><strong>Establishment of a feedback optimization mechanism<\/strong><\/p>\n<p>\/continuously increasing effect<\/p>\n<p><strong>Feedback dimensions:<\/strong>(a) Designing scorecards, which rate \"accompany demand, format accuracy, data credibility, landing\"<\/p>\n<p><strong>Feedback process:<\/strong><\/p>\n<ul>\n<li>Fill in the score sheet after the use of Prompt by members to indicate problem points<\/li>\n<li>Weekly wrap-up meetings to share high-scoring and problem cases<\/li>\n<li>DEVELOP GENERIC SOLUTIONS FOR HIGH-FREQUENCY (HF) PROBLEMS (E.G. AI-GENERATED DATA)<\/li>\n<li>Incentive mechanisms: scoring performance or learning resources for members who contribute high-quality cases and make valid recommendations\u3002<\/li>\n<\/ul>","protected":false},"excerpt":{"rendered":"<p>Prompt is what many people think is \"Prompt = directive to AI\" and ignores Prompt as a set of people working with AI. It is not only about \u201cdoing what\u201d, it is more about defining \u201cwhy\u201d, \u201cdo what\u201d, \u201cdo what\u201d, and what\u201d, which is essentially about translating people's vague needs into AI's understandable and implementable structural tasks. From a technical logic, Prompt ' s central role is to guide LLM in the mobilization of pre-training knowledge through input sequences, to focus on specific mission scenarios and to generate outputs that meet expectations. The LLM itself has no experience in the world of work, nor can it be read, and the director's proposal to make a sea trip to his subordinates may imply that he's more interested in the South-East Asian market than he has mentioned before<\/p>","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[144],"tags":[192,837],"collection":[302],"class_list":["post-48131","post","type-post","status-publish","format-standard","hentry","category-baike","tag-prompt","tag-837","collection-prompt"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/posts\/48131","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/comments?post=48131"}],"version-history":[{"count":0,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/posts\/48131\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/media?parent=48131"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/categories?post=48131"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/tags?post=48131"},{"taxonomy":"collection","embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/collection?post=48131"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}