UN
UN × Google Initiative Reference

AI Data Readiness Workbench

Local Audit Engine Active

Addressing statistical retrieval failure in AI agents:

UNICEF found leading AI models frequently hallucinate or confuse key figures when querying raw PDF/prose datasets. This tool evaluates chunk retrieval accuracy, precision scoring, and JSON schema readiness.

1. Source Statistical Corpus

~48 Tokens
Granularity:

Semantic Chunks & Extracted Entities

3 Chunks

Agent-Ready Canonical Schema

UN-Google Schema compatible key-value indicators


        

2. Agent Query Simulator & Audit Engine

Natural Language Retrieval
Quick Prompts:
Benchmark Telemetry

Evaluation in Progress

--/100 Agent Readiness
Confidence Score 0.00
Exact Match -- Grounding verified
Retrieval Status Pending Vector affinity
Hallucination Risk Low (8%) Grounded in context
Ground Truth Chunk Retrieved Cosine Sim: 0.00
Execute a query to inspect ground truth matching.

Agent Response Synthesis

Waiting for query...

Why UNICEF standardizes statistics for AI:

Generalist LLMs commonly confuse regional aggregates with global sums (e.g. interpreting a 78.4% sub-Saharan Africa childhood vaccine coverage as the worldwide rate). Converting unstructured text into atomic semantic vectors and strict UN metadata schemas ensures automated policy advisors receive clean factual boundaries.

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