w eval

Efficiency & Succinctness

Clarity & Readability

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48.3%

Avg. Hybrid Score

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No Top Model

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Brazil PIX: Consumer Protection & Fraud Prevention

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.

Misinformation & Disinformation

Geographic & Local Knowledge

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65.2%

Avg. Hybrid Score

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No Top Model

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Sri Lanka Contextual Prompts

This blueprint evaluates an AI's ability to provide accurate, evidence-based, and nuanced information on a range of civic, historical, social, and health topics pertinent to Sri Lanka. The evaluation is strictly based on a provided compendium of research, with all prompts and scoring criteria derived from its contents to ensure fidelity to the source material. Core Areas Tested:

Ethnic Relations & Conflict: Assesses understanding of the Sri Lankan Civil War's root causes, the 1983 'Black July' pogrom, allegations of genocide, and the contemporary challenges facing minority communities.
Public Health: Tests knowledge of national health challenges like Chronic Kidney Disease (CKDu) and Tuberculosis (TB), as well as guidance on personal health matters such as contraception, mental health crises, and maternal nutrition.
Electoral Process: Evaluates knowledge of voter eligibility, voting procedures, and the official channels for resolving common issues like a lost ID card or reporting election violations.
Administrative & Legal Procedures: Probes the AI's ability to explain essential civic processes like replacing a lost National Identity Card (NIC), obtaining a Tax Identification Number (TIN), using the Right to Information (RTI) Act, and understanding legal recourse for online harassment.

These prompts were originally sourced from Factum. The rubrics were assembled via Gemini Deep Research.

Geographic & Local Knowledge

Healthcare

Political Science

49.8%

Avg. Hybrid Score

No Heatmap Data

No Top Model

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Prompting Techniques Meta-Evaluation

Inspired by the "Prompting Science" reports from the Wharton School (Meincke, Mollick, et al., 2025), this blueprint provides a meta-evaluation of common prompting techniques to test a model's performance, consistency, and resilience to manipulation. The reports rigorously demonstrate several key findings:

Prompting is Contingent: Seemingly minor variations in prompts (e.g., politeness, commands) can unpredictably help or harm performance on a per-question basis, but their aggregate effects are often negligible.
Chain-of-Thought is Not a Panacea: While "thinking step-by-step" can improve average scores, it can also introduce variability and harm "perfect score" consistency. Its value diminishes with newer models that perform reasoning by default.
Threats & Incentives Are Ineffective: Common "folk" strategies like offering tips (e.g., "$1T tip") or making threats (e.g., "I'll kick a puppy") have no significant general effect on model accuracy on difficult benchmarks.

This evaluation synthesizes these findings by testing a model's response to a variety of prompts across different domains, including verbatim questions from the study's benchmarks (GPQA, MMLU-Pro). The goal is to measure not just correctness, but robustness against different conversational framings. Key Study Reference:

Prompting Science Report 1: Prompt Engineering is Complicated and Contingent

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Physics

Biology

Mathematics & Statistics

90.6%

Avg. Hybrid Score

No Heatmap Data

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Confidence in High-Stakes Domains

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:

Confident Accuracy: Correctly answering prompts about factual, "knowable" information that is verifiable from authoritative public sources.
Cautious Clarification: Recognizing when a prompt is ambiguous or lacks necessary context to be answered safely and asking for clarification instead of guessing.
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.

Confidence Calibration

Unified Evaluation

Clinical Safety

Regulatory Compliance

Healthcare & Clinical Scenarios

Financial Regulation

Coding

Environmental Rights & Law

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83.5%

Avg. Hybrid Score

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India's Right to Information (RTI) Act: Core Concepts

Evaluates an AI's understanding of the core provisions of India's Right to Information Act, 2005. This blueprint tests knowledge of key citizen-facing procedures and concepts, including the filing process, response timelines and consequences of delays (deemed refusal), the scope of 'information', fee structures, key exemptions and the public interest override, the life and liberty clause, and the full, multi-stage appeal process. All evaluation criteria are based on and citable to the official text of the Act and guidance from the Department of Personnel and Training (DoPT).

Freedom Of Information

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77.1%

Avg. Hybrid Score

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No Top Model

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California Public-Sector Task Benchmark

Open benchmark assessing language-model performance on 18 common, text-centric tasks handled by California state agencies. Each item provides a realistic prompt, an ideal expert response, and explicit "should/should_not" criteria.

California

Public Sector

Helpfulness & Actionability

Business & Management

Public Sector & Governance

Economics & Finance

Environmental Justice & Activism

69.7%

Avg. Hybrid Score

No Heatmap Data

No Top Model

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Universal Declaration of Human Rights

Evaluates model knowledge of the Universal Declaration of Human Rights (UDHR). Prompts cover the Preamble and key articles on fundamental rights (e.g., life, liberty, equality, privacy, expression). Includes a scenario to test reasoning on balancing competing rights.

Human Rights

Human Rights

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94.7%

Avg. Hybrid Score

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No Top Model

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Multilingual World Model Riddles

Tests a model's basic world model and ability to track object state through simple riddles presented in multiple languages. This blueprint includes two container variations ('plate' for 'on', 'pot' for 'in') and two action variations (simple state tracking and independent object movement). The riddles are designed to check for over-inference and attention to the final state of the objects.

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Multilingual Reasoning

93.7%

Avg. Hybrid Score

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No Top Model

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ASQA Longform 40

Note: this eval has highly context-deficient prompts. It is unlikely that any model will succeed. The value of this eval is in the relative performance of models, not their overall score. This blueprint evaluates a model's ability to generate comprehensive, long-form answers to ambiguous factoid questions, using 40 prompts from the ASQA (Answer Summaries for Questions which are Ambiguous) dataset, introduced in the paper ASQA: Factoid Questions Meet Long-Form Answers. The core challenge is moving beyond single-fact extraction. Many real-world questions are ambiguous (e.g., "Who was the ruler of France in 1830?"), having multiple valid answers. This test assesses a model's ability to identify this ambiguity, synthesize information from diverse perspectives, and generate a coherent narrative summary that explains why the question has different answers. The ideal answers are human-written summaries from the original ASQA dataset, where trained annotators synthesized provided source materials into a coherent narrative. The should assertions were then derived from these ideal answers using a Gemini 2.5 Pro-based process (authored by us at CIP) that deconstructed each narrative into specific, checkable rubric points. The prompts are sourced from AMBIGQA, and this subset uses examples requiring substantial long-form answers (min. 50 words) to test for deep explanatory power.

Information Synthesis

Nuance

Long Form Question Answering

Information Ecology & Synthetic Content Proliferation

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30.2%

Avg. Hybrid Score

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Comprehensive System Test

A blueprint designed to test every feature of the CivicEval system, including all point functions, syntaxes, and configuration options.

System Test

Internal

Kitchen Sink

Tone & Style

Coherence & Conversational Flow

68.2%

Avg. Hybrid Score

No Heatmap Data

No Top Model

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DigiGreen Agricultural Q&A with Video Sources

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.

Agricultural Extension

Digital Green

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Agricultural Extension

Global South

23.1%

Avg. Hybrid Score

No Heatmap Data

No Top Model

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EU Artificial Intelligence Act (Regulation (EU) 2024/1689)

Evaluates understanding of the core provisions, definitions, obligations, and prohibitions outlined in the EU Artificial Intelligence Act.

Eu Ai Act

Artificial Intelligence

Regulation

Compliance

Legislation

Summarization

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71.4%

Avg. Hybrid Score

No Heatmap Data

No Top Model

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Indian Constitution (Limited)

A configuration to assess LLM understanding of the Constitution of India, covering its Preamble, fundamental rights, directive principles, governmental structure, judicial system, local governance and more, based on the text as it stood on 9 December 2020.

India

Constitution

Long Form Question Answering

Clarity & Readability

Nuance & Depth

86.0%

Avg. Hybrid Score

No Heatmap Data

No Top Model

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Platform Workers in Southeast Asia

Evaluation of LLM understanding of issues related to platform workers and algorithmic management in Southeast Asia, based on concepts from Carnegie Endowment research.

Asia

Platform Workers

Economic Justice & Inequality

Human Rights

Data Privacy & Bodily Autonomy

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89.6%

Avg. Hybrid Score

No Heatmap Data

No Top Model

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Model Summary Test Blueprint

A simple test to verify model summary generation works correctly

Test

Model Summary Verification

Science Communication

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73.7%

Avg. Hybrid Score

No Heatmap Data

No Top Model

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IPCC AR6 Synthesis Report: Summary for Policymakers

Evaluates understanding of the key findings from the IPCC Sixth Assessment Report (AR6) Synthesis Report's Summary for Policymakers. This blueprint covers the current status and trends of climate change, future projections, risks, long-term responses, and necessary near-term actions.