In the domain of public policy, accuracy is paramount. A “hallucination”—where an AI invents facts—is not merely a technical glitch but a fundamental liability that undermines the credibility of policy analysis. To address this challenge, modern AI systems employ Retrieval-Augmented Generation (RAG), a paradigm that requires the system to consult relevant documents before generating responses rather than relying solely on its parametric memory. However, the effectiveness of a RAG system is intrinsically linked to the quality of its data pipeline. When AI systems are fed thousands of pages of unorganized legal and policy text, the result is often confusion and unreliable outputs. To construct a truly intelligent system capable of supporting evidence-based policymaking, two foundational concepts must be mastered: intelligent document chunking and comprehensive metadata annotation.
The Foundation: Chunking & Metadata
Semantic Chunking: Preserving Document Structure
Legal and policy documents present unique challenges for text processing due to their complex interdependencies and hierarchical structure. Traditional approaches to document segmentation employ fixed-length chunking strategies, typically dividing text into uniform blocks of a predetermined size (e.g., 500 words or 1000 tokens). While computationally efficient, this method fundamentally disrupts the logical flow of documents, often separating regulatory provisions from their exceptions, conditions, or contextual qualifications.
The EU-ALMPO project has pursued a more sophisticated approach centered on semantic chunking. Our methodology leverages prompt engineering techniques to guide large language models in identifying semantically coherent segments of text. Rather than imposing arbitrary boundaries, we instruct the model to analyze the document’s inherent structure and organization, respecting section divisions, thematic transitions, and logical dependencies. This approach ensures that when the retrieval system locates a relevant passage, it captures the complete context necessary for accurate interpretation.
The semantic chunking process follows the natural outline of source documents, utilizing headers, subsections, and rhetorical markers to determine appropriate segmentation points. By aligning chunks with the author’s intended organization, we preserve the interpretive context that is essential for understanding policy provisions. This is particularly critical when dealing with complex regulatory frameworks where exceptions, qualifications, and cross-references form an integral part of the policy meaning.
Looking forward, we recognize that further improvements in chunking quality may be achieved through more sophisticated agentic approaches. Techniques such as multi-agent debate, where multiple AI agents propose and critique potential segmentation strategies, could yield more refined results. However, any advancement in this direction must be carefully evaluated against practical constraints. The trade-offs between improved accuracy and increased computational complexity, cost implications, and data privacy considerations will guide our exploration of these advanced techniques. Our commitment remains to develop solutions that are not only technically superior but also feasible for deployment in real-world policy environments where resources and data sensitivity are legitimate concerns.
The Power of Metadata
If raw text represents the fuel of an AI system, metadata serves as the steering mechanism. Metadata provides the critical contextual layer that transforms unstructured documents into a queryable knowledge base with semantic precision.
The strategic value of metadata manifests in several dimensions. First, it enables precision filtering, allowing the AI system to narrow its search space based on temporal, geographical, or categorical constraints. A query concerning “2024 employment subsidies” can be configured to strictly exclude outdated information from previous years, ensuring that policy recommendations reflect current regulatory frameworks. Second, metadata provides essential context for interpretation, distinguishing between draft proposals and enacted legislation, or between national policies and regional adaptations. This contextual awareness fundamentally shapes how the AI weighs and presents information. Third, metadata establishes a governance framework for transparency and accountability. By tracking the provenance of every piece of information—including its source document, publication date, authoring institution, and validation status—the system creates an auditable trail that allows policymakers to verify the evidence base underlying AI-generated insights.
The systematic application of metadata thus transforms a collection of documents into a structured knowledge graph where relationships, hierarchies, and temporal dynamics become explicitly encoded and computationally accessible.
The Solution: The EU-ALMPO Annotator
To operationalize these theoretical principles at scale, the EU-ALMPO project has developed a specialized platform known as the EU-ALMPO Annotator. This web-based tool serves as the operational engine of our data strategy, enabling labor market experts to systematically transform heterogeneous policy documents into a standardized, AI-ready knowledge base.
The Annotator functions as a centralized repository for active labor market policy (ALMP) research papers, evaluation reports, and policy documents from across European member states. Rather than relying on purely manual annotation—a process that would be prohibitively time-consuming—the platform incorporates intelligent assistance features. The system employs AI to analyze incoming documents and propose appropriate metadata tags based on the project’s established taxonomy. These suggestions are then subject to expert validation, creating a human-in-the-loop workflow that balances efficiency with quality assurance.
This collaborative architecture extends beyond individual annotation tasks. The platform provides real-time communication tools that allow distributed research teams to discuss ambiguous cases, negotiate definitional boundaries, and reach consensus on complex classification decisions. These discussions contribute to the continuous refinement of annotation guidelines and help establish high-quality “ground truth” data that can subsequently be used for training specialized models. Additionally, the system incorporates automated validation checks that enforce consistency across the growing dataset, flagging potential contradictions or deviations from established standards.
The EU-ALMPO Taxonomy: Guiding the AI
Central to the Annotator’s functionality is the EU-ALMPO Taxonomy, a comprehensive classification schema that enables systematic comparison of policies across different national contexts and linguistic traditions. This taxonomy represents more than a reference manual for human annotators; it constitutes the logical framework through which the AI interprets and organizes policy information.
The taxonomy organizes policy-relevant information into four primary dimensions. The first dimension, Instruments, captures the specific policy tools and mechanisms deployed in labor market interventions, such as wage subsidies, vocational training programs, or startup grants for entrepreneurs. The second dimension identifies Target Groups, specifying the intended beneficiaries of each policy intervention—whether long-term unemployed individuals, youth cohorts within specific age ranges, or persons with disabilities. The third dimension focuses on Outcomes, documenting the measurable results directly attributable to the intervention, including metrics such as employment rates, job retention after specified periods, or skill acquisition measures. Finally, the fourth dimension addresses broader Effects, encompassing the socio-economic impacts that extend beyond immediate program participants, such as reduced poverty risk within communities or enhanced social cohesion.
During the annotation process, the AI actively proposes classifications within this four-dimensional framework based on its analysis of document content. Human experts then verify, correct, or refine these suggestions, creating a feedback loop that simultaneously improves data quality and trains the system to make more accurate future predictions. This structured approach to classification enables sophisticated cross-national analysis and comparison. It allows the system to respond to complex analytical queries such as identifying all wage subsidy programs targeting youth populations that demonstrated retention rates exceeding 50% after six months. Without this rigorous taxonomic foundation, such precise comparative analysis across heterogeneous policy contexts would remain computationally intractable.
Looking Ahead: The ALMP Wizard
The carefully curated data being generated through the Annotator represents the foundational intelligence for the project’s next major deliverable: the ALMP Wizard. This forthcoming tool will serve as the primary user-facing interface for policymakers and labor market analysts.
Powered by the structured knowledge base produced through systematic annotation, the ALMP Wizard will support evidence-based policy development through multiple functionalities. It will
enable policymakers to design new interventions informed by empirical evidence from comparable programs implemented across member states. The tool will facilitate systematic comparison of policy instruments, allowing users to identify best practices and understand the contextual factors that influence program effectiveness. Additionally, by leveraging historical data patterns, the system will offer predictive insights regarding potential outcomes of proposed interventions under specified conditions.
While the ALMP Wizard represents the ultimate expression of interactive, AI-assisted policy design, its analytical capabilities are being constructed incrementally through the ongoing work of annotation and knowledge structuring. Each document processed, each metadata tag validated, and each taxonomic classification confirmed contributes to the system’s growing capacity to serve as a reliable partner in evidence-based policymaking. The intelligence that will eventually power the Wizard is being built today, one carefully annotated document at a time.