The provided JSON configuration outlines a detailed prompt for generating a comprehensive entry for a flavor and fragrance material, specifically alpha-farnesene (CAS: 502-61-4), for FlavScents.com. This prompt is designed for use with a language model to produce a technically accurate and insightful document for professionals in the flavor and fragrance industry. Below is a breakdown of the key components and requirements of the prompt:
Key Components:
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Material Type Handling:
- The prompt distinguishes between single chemical compounds and complex natural materials, providing specific instructions for each type.
- For single compounds, detailed chemical information is required, while for complex materials, a discussion of key constituents and variability is necessary.
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Depth Requirement:
- Each section must be substantively filled, with specific word count targets to ensure depth and detail.
- Missing data should be acknowledged, and best-practice guidance should be provided instead of fabricating information.
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Output Format:
- The output should be structured with numbered headings and include a "Citation hooks:" line for each section to indicate sources for further reference.
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Sections:
- The prompt specifies 11 sections, each with distinct content requirements, ranging from chemical identity to practical insights for formulators.
- A QA Check section is mandatory to ensure all requirements are met before finalizing the output.
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Quality Assurance:
- The QA section requires a checklist to confirm the presence of all sections, citation hooks, and specific content like ppm ranges in the flavor section and exposure routes in the toxicology section.
Purpose and Use:
- Audience: The prompt is tailored for experienced professionals such as flavor chemists, perfumers, and regulatory specialists.
- Clarity and Accuracy: Emphasis is placed on providing clear, accurate, and relevant information, avoiding marketing language and focusing on interpretive insights.
- Regulatory and Safety Context: The prompt ensures that regulatory status and safety considerations are thoroughly covered, reflecting regional differences and exposure routes.
Application:
This prompt is intended for use with a language model like GPT-4.1 to generate detailed entries for FlavScents.com, ensuring that the content is both informative and aligned with industry standards. The structured approach and emphasis on citation hooks facilitate the integration of authoritative sources, enhancing the reliability and utility of the generated content for professional users.
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 2026-02-12 16:13:15 GMT (p2)