The provided JSON configuration outlines a detailed prompt for generating a comprehensive entry for a flavor and fragrance material, specifically 3-phenyl propionic acid (CAS: 501-52-0), for FlavScents.com. This prompt is designed for a technical research assistant and emphasizes the importance of clarity, accuracy, and relevance to formulation and safety contexts. Here’s a breakdown of the key components and requirements:
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 focus on key constituents and variability is emphasized.
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Depth Requirement:
- Each section must be substantively filled, with specific word count targets to ensure depth and thoroughness.
- Missing data should be acknowledged, and best-practice guidance should be provided instead of fabricating information.
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Output Format:
- The output must follow a structured format with numbered headings and include a "Citation hooks" line for each section to indicate sources for further consultation.
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Sections:
- The entry must include sections on identity, sensory profile, natural occurrence, use in flavors and fragrances, regulatory status, safety considerations, practical insights, and confidence notes.
- A QA Check section is required to ensure all components are present and correct.
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Quality Assurance:
- The QA section acts as a checklist to confirm the presence of all required sections, citation hooks, ppm ranges in the flavor section, coverage of exposure routes in toxicology, and regional regulatory mentions.
Constraints and Style:
- The writing should be tailored for experienced professionals, avoiding marketing language and focusing on interpretive insights.
- The prompt specifies the use of authoritative sources and internal references, with a clear hierarchy of source priority.
Application:
This configuration is intended for use with a language model (e.g., GPT-4.1) to generate detailed and reliable entries for FlavScents.com, ensuring that the information is both technically accurate and practically 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 2026-02-06 20:23:14 GMT (p2)