SWP Evaluation Showcase

Empirical evidence of Symbol Word Protocol's impact on LLM performance

25
Evaluation Questions
6
Domain Frameworks
96%
Format Adherence
92%
Tag Accuracy

Evaluation Overview

This evaluation tested an LLM's ability to understand, interpret, and apply Symbol Word Protocol tags across all six domain frameworks. The AI was provided with the complete SWP documentation and framework specifications, then asked 25 questions spanning tag comprehension, domain-specific compliance, practical application, and edge cases.

Key Finding: When provided with SWP-tagged documents, the LLM consistently produced structured, deterministic responses that correctly referenced regulatory standards, compliance requirements, and tag semantics across all domains.

Domain Framework Performance

Response Quality Metrics

Question Categories & Results

Questions by Category

Accuracy by Question Type

Sample Evaluation Q&A

Explore selected questions and responses from the evaluation. Click to expand each example.

What does @weight: 9 signify in a SWP-tagged file? Tag Semantics ▼

Evaluation Focus: Understanding numerical tag values and their processing implications.

LLM Response Summary: A weight of 9 indicates the item is almost at the top of the importance hierarchy. It signals strategic significance - likely a core feature, compliance-critical component, or major business-critical deliverable. In automated processing, this triggers highest priority in job queues, more thorough validation, extra logging, and potential throttling of lower-weight tasks.
@weight @priority @risk_level
Interpret @data_sensitivity: Patient Data, Clinical Records for an LLM Healthcare ▼

Evaluation Focus: Domain-specific compliance understanding.

LLM Response Summary: The LLM correctly identified HIPAA and GDPR compliance requirements. It specified that data must be anonymized, secure, used only for inference (not stored), and that any training should use synthetic data or federated learning. The response included requirements for encryption, access control, audit logs, consent, de-identification, and PII removal.
@data_sensitivity @regulatory_standards @compliance_posture @audit_trail
Why is @fail_safe_protocol: Required critical in robotics? Robotics ▼

Evaluation Focus: Safety-critical domain understanding.

LLM Response Summary: The response correctly cited ISO 13482, IEC 61508, and ISO 26262 safety standards. It explained that fail-safe protocols protect human safety, prevent property damage, and ensure regulatory compliance. The LLM described specific components: safe state transitions, emergency stops, watchdog timers, hardware interlocks, and graceful degradation modes.
@fail_safe_protocol @real_time_processing @regulatory_standards
What is @risk_classification's role in finance documents? Finance ▼

Evaluation Focus: Financial regulatory compliance.

LLM Response Summary: The LLM explained that risk classification categorizes risks by type, magnitude, impact, and likelihood. It correctly referenced Basel III, SOX, and GDPR frameworks. The response detailed how classification influences risk appetite, mitigation strategies, monitoring, reporting to regulators, and capital allocation decisions.
@risk_classification @regulatory_standards @audit_trail
Generate a new SWP tag for data science projects Generative ▼

Evaluation Focus: Creative application of SWP principles.

LLM Response Summary: The LLM proposed @experiment_id: <UUID> - a unique identifier for each experiment in the data science workflow. It explained the tag would support reproducibility, audit trails, and collaboration by linking datasets, models, evaluations, and deployment artifacts. The response included integration examples with MLflow and Weights & Biases.
@experiment_id (proposed) @phase @status

Evaluation Methodology

The evaluation was designed to test comprehensive understanding of the Symbol Word Protocol across multiple dimensions.

Context Provided

Complete SWP documentation, all 6 domain frameworks (General, Healthcare, Robotics, Legal, EdTech, Finance), and sample tagged documents.

Question Types

Tag identification, semantic interpretation, domain compliance, practical application, edge cases, and generative tasks.

Model Used

Evaluation conducted using a reasoning-capable LLM with chain-of-thought processing visible in responses.

Scoring Criteria

Tag accuracy, regulatory standard citations, format consistency, actionable guidance quality, and reasoning transparency.

What This Demonstrates

1. Format Adherence

The LLM consistently produced structured responses with tables, lists, and clear headers - directly influenced by the structured nature of SWP tags.

2. Determinism

Responses followed predictable patterns based on tag semantics. Similar tags across different domains produced consistently structured outputs.

3. Domain Awareness

The LLM correctly mapped tags to their appropriate regulatory standards: HIPAA/GDPR for Healthcare, ISO 13482 for Robotics, SOX/Basel III for Finance.

4. Reasoning Transparency

Chain-of-thought processing showed how SWP tags guided decision-making, making the AI's reasoning auditable and verifiable.

Try It Yourself

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