Use LLMs to create data models for Open Products Facts categories

[teolemon]

What

• Use LLMs to create data models for Open Products Facts categories
  Can you list as bullet points the relevant data fields to describe "Molecular diagnostics analyzer" for a product datasheet.

• Product Name: Molecular Diagnostics Analyzer

• Application: [specify the type of sample analysis, such as clinical or research]

• Technology: [indicate the molecular diagnostic technology used, such as PCR, NGS, etc.]

• Sample type compatibility: [list the types of samples that can be analyzed, such as blood, saliva, etc.]

• Detection methods: [indicate the types of targets the analyzer can detect, such as DNA, RNA, pathogens, etc.]

• Throughput: [indicate the number of samples that can be processed per unit of time, such as per hour or day]

• Run time: [specify the total time required for a sample analysis, from sample preparation to result generation]

• Sensitivity: [indicate the minimum concentration of the target that can be detected]

• Specificity: [indicate the degree of accuracy in detecting the target of interest, as well as avoiding false positive results]

• Data management: [indicate the type of data storage and analysis capabilities, such as cloud storage, real-time data analysis, etc.]

• User interface: [describe the type of interface for sample analysis, such as touchscreen or computer-based]

• Power source: [indicate the type of power required, such as AC power or battery-operated]

• Dimensions: [provide the physical dimensions of the analyzer, such as height, width, and depth]

• Weight: [indicate the weight of the analyzer for ease of transport and handling]

• Brand: [name of the brand producing the product]

• SKU or product code: [unique identifier for the product]

• MSRP: [manufacturer's suggested retail price].

Originally posted by @teolemon in #272 (comment)

Part of

• Leverage Generative AI across Open Food Facts (tracker) #289