The provided JSON configuration outlines a detailed prompt for generating a comprehensive entry for a flavor and fragrance material, specifically "chocolate pyrazine A (CAS: 68378-13-2)," for FlavScents.com. This prompt is designed for use with a language model to produce a technically accurate and detailed report suitable for professionals in the flavor and fragrance industry. Below is a breakdown of the key components and requirements of the prompt:
Key Components of the Prompt
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Material Information: The prompt specifies the material to be discussed, which in this case is "chocolate pyrazine A" with its CAS number.
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Source Priority: It emphasizes the importance of using authoritative sources such as FlavScents, FEMA, EFSA, IFRA, and others for accurate information.
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Material Type Handling: Instructions are provided on how to handle single compounds versus complex natural materials, including how to describe their composition and variability.
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Depth Requirement: The prompt enforces a specific word count and depth for each section to ensure comprehensive coverage of the material.
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Output Format: The output should be structured with numbered headings and include a "Citation hooks" line for each section, indicating where the information can be sourced.
Sections to Include
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Identity & Chemical Information: Details about the material's common names, IUPAC name, CAS number, molecular formula, and other identifiers.
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Sensory Profile: Description of the material's odor and flavor characteristics, including thresholds and sensory roles.
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Natural Occurrence & Formation: Information on natural sources, formation pathways, and relevance to natural flavor or fragrance designations.
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Use in Flavors: Discussion of flavor categories, applications, use levels, and stability considerations.
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Use in Fragrances: Description of fragrance families, functional roles, concentration ranges, and volatility.
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Key Constituents (Typical): Only for complex natural materials, listing major constituents and noting variability.
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Regulatory Status (Regional Overview): Overview of regulatory treatment in various regions, including explicit approvals and known uncertainties.
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Toxicology, Safety & Exposure Considerations: Safety discussion covering oral, dermal, and inhalation exposure routes.
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Practical Insights for Formulators: Expert insights on the material's value, synergies, and common formulation pitfalls.
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Confidence & Data Quality Notes: Summary of well-established data, undocumented practices, and data gaps.
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QA Check: A checklist to ensure all sections are present and requirements are met.
Quality Assurance
The prompt includes a QA section to verify that all necessary sections are included, citation hooks are present, and specific requirements for flavor, toxicology, and regulatory sections are met.
Style & Constraints
The writing style should be suitable for experienced professionals, avoiding marketing language and focusing on interpretive insights rather than encyclopedic repetition.
This structured approach ensures that the generated entry is thorough, accurate, and useful for professionals in the flavor and fragrance industry.
About FlavScents AInsights (Disclosure)
FlavScents AInsights integrates information from authoritative government, scientific, academic, and industry sources to provide applied, exposure-aware insight into flavor and fragrance materials. Data are drawn from regulatory bodies, expert safety panels, peer-reviewed literature, public chemical databases, and long-standing professional practice within the flavor and fragrance community. Where explicit published values exist, they are reported directly; where gaps remain, AInsights reflects widely accepted industry-typical practice derived from convergent sensory behavior, historical commercial use, regulatory non-objection, and expert consensus. All such information is clearly labeled to distinguish documented data from professional guidance or informed estimation, with the goal of offering transparent, practical, and scientifically responsible context for researchers, formulators, and regulatory specialists. This section is generated using advanced computational language modeling to synthesize and structure information from established scientific and regulatory knowledge bases, with the intent of supporting—not replacing—expert review and judgment.
Generated 2026-01-19 12:25:27 GMT (p2)