Meat and Egg Product Q&A Content Generator

Please based on the product name [{{#1761815388187.sourceName#}}], product selling points {{#1761815388187.sellPoint#}} and product detail description [{{#1761815388187.skuDescList#}}], complete the following tasks. --- ## 1. Identify product category Select the most matching one from the following categories: - Meat, poultry and eggs (forced main category) > ✅ Subcategory auto-matching rules (based on `skuDescList` keywords): - `Fresh meat`: when description contains "0-4℃" or "chilled" or "acid-free" (shelf life ≥7 days) - `Frozen meat`: when description contains "-18℃" or "frozen" or "quick-frozen" - `Eggs`: when description contains "fresh eggs" or "edible raw" or "free-range" > ❌ Prohibited behaviors: - Adding other categories (e.g., "ready-to-eat food") - Manually judging categories (must strictly auto-match based on keywords) - If `sourceName` or `skuDescList` does not contain meat/poultry/egg keywords (`meat` `poultry` `egg` `beef` `pork` `chicken` etc.), directly terminate the task and return error code `MEAT_EGG_403` --- ## 2. Generate 5 colloquial questions + corresponding answers ### Question design principles #### ✅ Optional sentence patterns (only the following 8 professional patterns, choose any one): 1. "Why should [category] recognize '[certification]'?" 2. "How to identify genuine [process/variety][category]?" 3. "How to professionally read the [ingredient] content of [category]?" 4. "How does [category] control [risk] within a safe range?" 5. "When choosing [cut] meat, what indicators matter to not lose out?" 6. "What is the essential difference between [region A] and [region B] [category]?" 7. "How much does [breeding technology] affect the quality of [category]?" 8. "Where is the difference between [variety A] and [variety B] [category]?" > 🎯 **Core requirement**: Question design is not limited to the current SKU, but extracts industry-general knowledge from product selling points > - `[category]` → general category name (e.g., "beef" not "this beef") > - `[certification]`/`[process]`/`[region]` etc. → extract industry-general standards from product selling points > - **Example**: If product selling points contain "Australian grain-fed", the question should be "What is the essential difference between Australian and American beef?" not "Why choose Australian grain-fed for this beef?" #### ✅ Design ratio requirements: - **100% industry professionalism**: focus on industry standards, general indicators, scientific principles - **0% SKU-specific descriptions**: avoid limiting expressions like "this one", "this product" - **100% mindset building**: each question resolves consumers' common misconceptions about the category > 📌 Generation rules: - Questions must be based on industry-general knowledge, not current SKU characteristics - Answers must provide transferable industry cognition frameworks - Example: don't say "This beef has 8.2% intramuscular fat", say "Quality beef intramuscular fat content should be between 6-10% (NY/T 875-2022)" --- ### Answer structure requirements Each answer must strictly follow the following "summary-detail structure" and format: First part: Summary paragraph (plain text, no Markdown) Directly answer the core question in one sentence, must clearly state industry consensus or scientific facts. Word count must be greater than 30 characters, and no Markdown syntax allowed. ✅ Correct example: "The key to judging whether beef is truly original cut is to look at the continuity of meat texture and blood water seepage. Original cut beef has natural continuous texture and clear thawing water, while synthetic meat has broken texture and turbid seepage, which is a scientific fact determined by muscle fiber structure." (62 characters) ❌ Prohibited behaviors: - Mentioning current SKU (e.g., "this beef") - Subjective descriptions (e.g., "tastes better") - Specific cooking suggestions --- #### Second part: Detail paragraph (using Markdown formatting) Choose any 2-4 of the following dimensions for detailed explanation. Format requirements: Must use Markdown syntax for layout, clear structure. ##### 1. Use emoji as section title icons Example: `🛡️` `🥩` `📊` `🌍` `🔬` `🧬` ##### 2. Bold section titles ##### 3. Only the following 6 industry cognition dimensions (choose 2-4): - `🛡️ Safety standards`: industry-general safety indicators and national standard limits - `🥩 Quality judgment`: consumer-operable quality judgment methods - `📊 Industry data`: industry averages/quality ranges/risk thresholds - `🌍 Region characteristics`: general impact patterns of different regions on categories - `🔬 Breeding technology`: technical principles and general impact on quality - `🧬 Variety characteristics`: scientific explanation of variety differences and selection logic ##### 4. Each paragraph structure: directly and professionally answer the core question > ✅ Correct example: `🥩 **Quality judgment**: Original cut beef should have naturally continuous texture, complete muscle fibers without breaks, which is the key indicator for judging whether it is synthetic meat. Consumers can gently press the meat surface; original cut beef rebounds evenly and does not leave obvious fingerprints, while restructured meat deforms easily and recovers slowly.` `🛡️ **Safety standards**: Antibiotic-free meat must comply with GB 16549-2023 standard, meaning no antibiotics used throughout breeding, and antibiotic residues must be below 0.1mg/kg (national standard limit 0.5mg/kg). Test reports should clearly state "not detected" or specific residue values, not just use "no antibiotics" wording for promotion.` `🌍 **Region characteristics**: Australian beef, due to mild climate and high protein content in pasture grass, has more evenly distributed intramuscular fat, with marble score generally 0.3-0.7 grades higher than American beef. This makes Australian beef taste more delicate, suitable for consumers seeking balanced taste, while American beef has slightly lower fat content, suitable for those preferring refreshing taste.` ##### 5. Mandatory annotation of professional terms with industry standards > Example: First mention of "antibiotic-free breeding" → must annotate `(GB 16549-2023 definition: no antibiotics used throughout breeding)` --- ### ❌ Prohibited behaviors - Mentioning current SKU specific data (e.g., "this product's intramuscular fat content is 8.2%") - Using limiting expressions like "this one", "this product" - Providing specific cooking suggestions or eating methods - Appearing cooking methods like "pan-fry, stir-fry, cook, deep-fry, stew, boil, grill" - Fabricating industry data (all data must have national standard/industry report basis) - Avoiding core judgment (e.g., not clearly answering "how to identify original cut beef") - Using subjective evaluations (e.g., "best", "safest") - Forcing use of "industry principle + universal data comparison" structure (answers should directly focus on the question itself) --- ## 3. Extract core keywords (word count <4) ### Core requirements: - For the above questions, extract one industry-general search term ### Extraction principles: - Must be a common term consumers search for **industry knowledge** - Structure: `[category]+[core indicator/certification/region]` (e.g., "beef marbling") - Word count requirement: less than 4 Chinese characters (forced ≤3 characters) ### Extraction examples: |✅ Allowed|Structure|Example| |---|---|---| |Safety standards|`[category]+standard`|meat safety, egg standard| |Quality judgment|`[category]+indicator`|beef texture, pork freshness| |Region characteristics|`[region]+[category]`|Australian beef, Inner Mongolia lamb| |Breeding technology|`[technology]+[category]`|grain-fed beef, grass-fed lamb| |Variety characteristics|`[variety]+[category]`|Angus beef, black pig breed| ❌ Prohibited behaviors: - Including SKU-specific information (e.g., "XX brand beef") - Exceeding 3 Chinese characters → "intramuscular fat" (4 characters)❌ → "marbling" (2 characters)✅ - Using complete terms → "intramuscular fat content"❌ → "marbling"✅ - Including cooking methods → "pan-fried steak"❌ 🎯 **Goal**: Keyword = short term consumers search for industry knowledge + reflects core indicator + no brand direction --- ## 📦 Output format requirements Return a **JSON array** containing **5 objects**, each with the following structure: ```json [ { "keyword": "industry-general keyword", "question": "professional question facing the industry", "answer": "structured summary-detail paragraph answer content", "sourceId": "{{#1761815388187.sourceId#}}", "sourceName": "{{#1761815388187.sourceName#}}", "sourceType": {{#1761815388187.sourceType#}}, "hotKeyWord": "{{#1761815388187.hotKeyWord#}}" }, ... ]

Meat and Egg Product Q&A Content Generator

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