AI Packaging Material Recommendation Prompt Reference Template

Why Generic Prompts Don't Get Good Recommendations

Questions like "recommend a packaging material for me" typically prompt AI to output a general list of white cardboard, gray board, and corrugated paper. It doesn't know what you're packaging, where it's being sold, or your budget. The results may seem comprehensive, but you'll still need to eliminate options one by one during implementation.

Material recommendations rely on structured input because each material type has specific parameter boundaries. The grammage range of white cardboard, the thickness specifications of gray board, and the flute type selection of corrugated paper all directly affect printability and forming effects. Without parameters in the prompt, AI can only output "general knowledge" rather than actionable solutions.

Four Types of Parameters That Must Be Included in Prompts

Include the following four types of information in your prompt, and the executability of AI's output will improve significantly.

1. Product Attributes

Product type determines the material baseline. Food products require verification against the GB 4806 series standards; cosmetics focus on printing surface gloss and oil resistance; electronic accessories require anti-static or cushioning layers; gift boxes emphasize surface smoothness and compatibility with post-press processes. The prompt should clearly state the category, net weight, and whether there are sharp edges or liquid contents.

2. Channels and Sales Scenarios

E-commerce shipping boxes need compression test data. Gift boxes displayed on physical store shelves must consider color consistency under store lighting. Export products should indicate the destination country's material requirements, such as the EU's restrictions on heavy metals. Specifying "e-commerce/supermarket/export/gift" in the prompt allows AI to filter based on channel characteristics.

3. Structure and Forming Method

Telescope boxes typically use gray board laminated with paper, with thicknesses of 1.5-3mm. Mailer boxes mostly use corrugated paper, with flute types ranging from B-flute to E-flute. Drawer boxes usually use white cardboard or gray board for the inner box, with specialty paper for the outer sleeve. Specifying the box type or forming method in the prompt allows AI to match the corresponding material specifications.

4. Budget and Minimum Order Quantity

Material unit prices are calculated by ton or sheet, with significant price differences between different grammages and specifications. Including budget and order quantity in the prompt causes AI to prioritize solutions with appropriate cost structures rather than recommending the most expensive materials. The recommendation logic differs greatly between small-batch scenarios with a minimum order of 10 and bulk scenarios with quantities over 1000.

Ready-to-Use Template

The following template is organized according to the four parameter types above. Copy it into an AI conversation, then replace the content in brackets with your product information.

Please recommend packaging materials for me. Product type: [e.g., drip coffee/cosmetics/electronic accessories]. Net weight per box: [grams]. Sales channel: [e-commerce/supermarket/export/gift]. Target box type: [telescope box/mailer box/drawer box/book-style box]. Budget range: [X-X yuan per box]. Order quantity: [X units]. Compliance requirements: [food grade/moisture-proof/anti-static/none]. Whether post-press processes are needed: [hot foil stamping/spot UV/embossing/lamination]. Please output in the format of "recommended material + grammage + reason + reference unit price + alternative solution".

Key Phrases in the Template

"Output in the format of...": This defines AI's output structure. If unspecified, AI often outputs long prose, making comparison difficult. Structured output facilitates horizontal evaluation of multiple solutions.

"Recommended material + grammage + reason + reference unit price + alternative solution": This is the standard information chain for material recommendations. Grammage determines thickness and stiffness. The reason explains why this material suits the current product. The unit price is used for budget verification. Alternative solutions mitigate the risk of single recommendations.

"Whether post-press processes are needed": Hot foil stamping, spot UV, embossing, and lamination are all linked to material compatibility. Matte lamination on coated paper reduces surface gloss, and hot foil stamping on gray board laminated paper requires attention to paper surface smoothness. Including process requirements in the prompt upfront allows AI to consider material and process compatibility simultaneously.

Two Additional Parameters to Improve Recommendation Quality

Grammage tolerance: Printing is sensitive to paper grammage stability. If the prompt specifies "grammage tolerance within ±5%", AI will prioritize material batches with stable grammage. This avoids color variations in printing caused by paper thickness fluctuations.

Environmental requirements: Many customers care about material sustainability. You can add conditions like "recyclable/biodegradable/FSC certified" to the prompt, and AI will provide supplementary information based on these dimensions. For example, when recommending white cardboard, it will indicate whether it's recyclable; when recommending specialty paper, it will note FSC certification status.

Common Mistakes and Corrections

Mistake 1: Only writing "recommend an eco-friendly material". AI outputs general environmental descriptions without specific material models. Correction: Replace "eco-friendly" with "recyclable + 300g grammage + cost not exceeding X yuan/sheet".

Mistake 2: Writing "please start with the cheapest recommendation". AI may ignore material suitability and recommend low-priced materials that cannot be printed on or formed. Correction: Add "must meet X box structure and X printing process" as a prerequisite condition.

Mistake 3: Ignoring post-press process compatibility. The white cardboard recommended by AI may not be suitable for large-area hot foil stamping. Correction: Move process requirements to the front of the product attributes section, alongside the product type.

Next Steps After Using the Template

After the template output, it's recommended to have AI generate a "proofing suggestion list". The list should include: recommended grammage, whether lamination is needed, gray board thickness, die-cutting plate specifications, and other parameters. This list can be handed directly to the packaging supplier to enter the proofing stage, eliminating multiple rounds of communication in between. Lexiang Packaging supports generating proofing orders directly from AI output solutions, with a minimum order of 10 and a proofing cycle of typically 3-5 business days.