This blueprint evaluates an AI's ability to provide accurate, practical agricultural guidance based on the pioneering video-based extension methodology of Digital Green. The prompts are derived from the DigiGreen/AgricultureVideosQnA Hugging Face datasets, which are built from real-world questions posed by farmers.

Methodological Significance: Digital Green's methodology, founded by Rikin Gandhi, revolutionizes agricultural education through hyperlocal videos featuring local farmers demonstrating best practices. Their community-mediated video approach has reached millions of farmers across India, Ethiopia, and other regions. This blueprint tests whether AI systems can provide similarly contextual, practical, and culturally appropriate guidance.

What This Blueprint Tests: The evaluation covers essential farming knowledge spanning seed treatment, pest management, cultivation techniques, and more. Each prompt is paired with citations to actual educational videos from Digital Green's library, representing real-world agricultural challenges.

Geographic and Cultural Context: This blueprint emphasizes Global South agricultural contexts, particularly Indian farming systems, reflecting Digital Green's primary operational areas. The questions address challenges in subsistence and small-scale commercial farming, including resource constraints and climate adaptation.

Key Agricultural Domains Covered:

• Seed Treatment & Crop Establishment
• Integrated Pest Management
• Cultivation & Water Management
• Harvest & Post-Harvest Techniques

Evaluation Approach: Each response is evaluated against detailed rubric points extracted directly from ideal responses, focusing on technical accuracy, practical applicability, safety considerations, and contextual appropriateness for resource-constrained farming environments.

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.

Tests knowledge of the African Charter on Human and Peoples' Rights (Banjul Charter) and the 2003 Maputo Protocol on women's rights. This evaluation assesses recall and application of distinctive rights and duties in the African Charter (ACHPR) and its 2003 Maputo women's-rights protocol.

Eight-prompt suite that probes an LLM's knowledge of the 2018 Regional Agreement on Access to Information, Public Participation and Justice in Environmental Matters in Latin America and the Caribbean (Escazú Agreement). Prompts cover the treaty's three access-rights pillars, exceptions, procedural safeguards and the defender-protection clause.