This book explores how Large Language Models can help public organizations turn previously unusable information into actionable insight. Government agencies collect enormous volumes of handwritten forms, PDFs, free-text responses, case notes, and other unstructured content, yet much of it remains difficult to analyze at scale. This book shows how LLMs, combined with OCR, computer vision, and related document-processing techniques, can extract structure and meaning from these data sources, helping public sector teams improve service delivery, operational efficiency, and evidence-based decision-making. It is written for data scientists, AI practitioners, public administrators, policymakers, researchers, and graduate students who want a practical and accessible guide to this fast-emerging field.

Unlocking Unstructured Data: Transforming Public Services with Large Language Models offers a distinctive public-sector perspective on both the technical and organizational challenges of deploying LLMs responsibly. It examines foundational concepts, implementation architectures, and evaluation frameworks, then moves into real-world case studies across healthcare, social services, taxation, regulatory compliance, and citizen engagement. Readers will also find guidance on governance, privacy, explainability, bias mitigation, and change management, making this a useful resource for anyone seeking to modernize government data workflows while maintaining trust, transparency, and accountability.