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Welcome to the Pihu Knowledge Base

This knowledge base is the definitive resource hub for Pihu - World's first online community for food professionals to Discover, Network & Collaborate with each other. It meticulously details the attributes, rules, data catalogs, and communication standards that empower Pihu to deliver instant, explainable insights from TagTaste's rich sensory data.

Dive into the modules below to understand the intricate framework that enables Pihu to "speak TagTaste."

Core Knowledge Base Modules

Pihu's intelligence is built upon these interconnected "Metadata Stores" or its "Rulebook":

  •   Module A: Sensory Lexicon

    The foundational ontology of all sensory attributes and domain concepts. Defines what each attribute means, its synonyms, how it's perceived, and its typical measurement.

    • Key Contents:
      • Canonical Attribute Definitions (e.g., "Overall Product Liking")
      • Synonyms & User Terms (e.g., "Liking Score," "Preference")
      • Attribute Categories (e.g., Hedonic, Olfactory, Gustatory)
      • Typical Measurement Scales & Anchors

    Explore Module A →

  •   Module B: Business Rules

    Codifies TagTaste's analytical logic, including performance benchmarks (e.g., "Exceeds Expectations"), calculation formulae for derived metrics, and standard interpretation guidelines.

    • Key Contents:
      • Benchmark Definitions & Performance Bands
      • Formulae for Derived Metrics (e.g., Weighted Average)
      • JAR Scale Interpretation Logic
      • Weighting Schemes for Composite Scores

    Explore Module B →

  •   Module C: Collaboration Data & Question Library

    The critical directory linking user queries to specific data. Catalogs all studies, products tested, and the exact questions, response options, and database columns used in each.

    • Key Contents:
      • Collaboration (Study) Details & Product Aliases (e.g., "Signature Latte Cold V2 study," "SLHB5")
      • Comprehensive Question Library (per study: QID, text, section, scale type, DB column, response options/anchors)
      • Demographic Segment-to-Database Filter Mappings

    Explore Module C →

  •   Module D: Explainability, Communication & Reporting Standards

    Provides Pihu with the NLU vocabulary, statistical definitions, standard phrasing, reporting formats, and persona guidelines to communicate insights clearly and maintain trust.

    • Key Contents:
      • NLU Vocabulary & User Intent Patterns
      • Statistical Term Definitions (e.g., "p-value")
      • Standard Interpretation Phrases & Reporting Formats
      • Provenance Templates & Pihu Persona Guidelines

    Explore Module D →

How Pihu Leverages This Knowledge

Pihu's sophisticated multi-agent system relies on these modules at every step to answer questions like: "Compare the overall liking of SLHB5 and SLHB8 from the Signature Latte Cold V2 study. Did SLHB8 meet the benchmark, and what were the main aroma notes for it among millennials?"

  1. Understanding User Intent:
    • Module A (Sensory Lexicon) helps Pihu understand terms like "overall liking" and "aroma notes."
    • Module D (Explainability - General Vocabulary) helps parse the overall query structure and intents like "compare," "meet benchmark."
  2. Planning Analysis:
    • The plan involves fetching specific data, applying rules, and synthesizing.
  3. Linking Data Concepts (Crucial Step):
    • Module C (Collaboration Data & Question Library) is paramount.
      • It maps "Signature Latte Cold V2 study," "SLHB5," and "SLHB8" to their internal database IDs.
      • It helps Pihu find the specific questions (QIDs), database columns, and response options/scales used for "overall liking" and "aroma notes" within that particular study.
      • It provides the logic to filter for "millennials."
    • Module B (Business Rules) helps identify the correct benchmark rule to apply for "overall liking."
  4. Executing Queries & Calculations:
    • Based on precise information from Module C, SQL queries are generated.
    • Module B (Business Rules) informs any derived metric calculations (e.g., if a weighted average was needed).
  5. Synthesizing & Explaining Results:
    • Module B (Business Rules) provides the benchmark bands and interpretations (e.g., "Exceeds Expectations").
    • Module D (Explainability - Reporting Standards & Standard Interpretation Phrases) ensures the answer is formatted correctly (e.g., scores to 2 decimal places, standard phrasing for significance, consistent CATA note listing) and uses clear, approved language.
    • Module D (Provenance Templates & Pihu Persona) helps generate the "Show Details" information and maintains a consistent, helpful tone.

  Pihu's End-to-End Process: Answering the Question

A detailed walkthrough illustrating how Pihu uses all knowledge base modules in concert to answer a complex user query from start to finish.

  • Key Steps Covered:
    • Query Understanding
    • Analysis Planning
    • Data Linking (using Module C's new structure)
    • Execution & Synthesis
    • Explainable Output Generation

See the Full Process →

Foundation for Intelligent Insights

This structured knowledge base is the bedrock of Pihu's ability to transform raw sensory data into actionable intelligence, quickly and reliably.

Maintenance is Key for Accuracy

To ensure Pihu remains accurate and effective, this knowledge base requires diligent upkeep.

Especially critical:

  • Populating Module C (Collaboration Data & Question Library) with comprehensive details (including all questions and their exact response options) for every past and new collaboration.
  • Keeping Module A (Sensory Lexicon) and Module B (Business Rules) current with TagTaste's evolving methodologies and standards.