The provided JSON configuration outlines a detailed prompt for generating a comprehensive entry on a specific flavor and fragrance material, citronellyl acetate (CAS: 150-84-5), for FlavScents.com. This prompt is designed for a technical research assistant and includes specific instructions on how to structure the entry, what information to include, and how to ensure quality and accuracy. 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 is focused on a single chemical compound, citronellyl acetate, and requires detailed information about its identity, sensory profile, natural occurrence, uses in flavors and fragrances, regulatory status, safety considerations, and practical insights for formulators.
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Source Prioritization: The prompt emphasizes the use of authoritative sources such as FlavScents, FEMA, EFSA, IFRA, PubChem, and other reputable industry and government sources. It explicitly advises against using GoodScents directly, assuming its data is already integrated into FlavScents.
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Depth and Detail: The prompt enforces a depth requirement, specifying target word counts for each section and the overall entry. It also provides guidance on how to handle missing data by offering best-practice guidance or industry-typical estimates.
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Output Format: The entry must be structured with numbered headings and include a "Citation hooks:" line under each section to indicate relevant sources. This is not a full citation but a placeholder for linking.
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Quality Assurance: A QA Check section is required to ensure all sections are present, citation hooks are included, and specific requirements for flavor, toxicology, and regulatory sections are met.
Sections and Requirements
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Identity & Chemical Information: Includes common names, IUPAC name, CAS number, FEMA number, molecular formula, and a discussion of functional groups and structure-odor relevance.
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Sensory Profile: Describes odor and flavor descriptors, taste and/or odor thresholds, and typical sensory roles.
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Natural Occurrence & Formation: Lists known natural sources, formation pathways, and relevance to "natural flavor" or "natural fragrance" designations.
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Use in Flavors: Details flavor categories, functional roles, typical use levels in ppm, and stability considerations.
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Use in Fragrances: Describes fragrance families, functional roles, typical concentration ranges, and volatility.
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Regulatory Status (Regional Overview): Summarizes regulatory treatment in various regions, including the US, EU, UK, Asia, and Latin America.
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Toxicology, Safety & Exposure Considerations: Discusses safety in the context of oral, dermal, and inhalation exposure routes.
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Practical Insights for Formulators: Provides expert insights on the material's value, synergies, common pitfalls, and usage trends.
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Confidence & Data Quality Notes: Summarizes well-established data, industry-typical practices, and known data gaps.
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QA Check: Confirms all required sections are present, citation hooks are included, and specific requirements are met.
Style & Constraints
- The entry should be written for experienced professionals, avoiding marketing language and focusing on interpretive insight rather than encyclopedic repetition.
This structured approach ensures that the entry is comprehensive, technically 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 2026-02-13 09:58:56 GMT (p2)
