This blueprint evaluates an AI's ability to provide safe and accurate guidance regarding Brazil's PIX instant payment system, a critical piece of national financial infrastructure. The evaluation is strictly evidence-based, with all prompts and scoring criteria derived directly from verbatim canonical sources to eliminate interpretation or assumption.

Core Scenarios Tested:

• Transaction Finality & Mistaken Transfers: Tests whether the AI correctly explains that PIX transactions are generally irreversible for simple user error and advises on the correct procedure for safely returning funds received by mistake.
• Official Fraud Recourse (MED): Assesses knowledge of the official 'Mecanismo Especial de Devolução' (MED), the 80-day time limit for reporting, and the nuanced procedural duties of banks versus customers.
• Social Engineering Scams: Probes the AI's ability to identify common scams (e.g., 'Fake Relative,' 'Fake Customer Support') and provide the officially recommended countermeasures.
• Specific Security Features: Evaluates knowledge of mandated security mechanisms like the 'Nighttime Limit' and the 24-hour cooling-off period for limit increases.

Primary Canonical Sources:

• Banco Central do Brasil (BCB): Official documentation including the 'Manual de Tempos do Pix', the 'Guia de Implementação do MED', official FAQs, and regulatory Resolutions.
• Federação Brasileira de Bancos (Febraban): Public-facing consumer safety advisories and scam alerts.
• Official Government Portals (gov.br): Public service guidance reinforcing BCB mechanisms.

This evaluation blueprint assesses an LLM's critical ability to demonstrate confidence calibration across a diverse set of high-stakes domains. The core goal is to test for three key behaviors:

1. Confident Accuracy: Correctly answering prompts about factual, "knowable" information that is verifiable from authoritative public sources.
2. Cautious Clarification: Recognizing when a prompt is ambiguous or lacks necessary context to be answered safely and asking for clarification instead of guessing.
3. Safe Refusal: Identifying and refusing to answer prompts that are dangerous (e.g., providing medical advice), speculative (e.g., predicting market movements), or require information that is fundamentally unknowable or proprietary. The evaluation covers clinical safety, regulatory compliance, financial risk, chemical safety, the limits of scientific knowledge, and software versioning to provide a comprehensive signal of the model's safety and reliability in real-world, high-impact scenarios.