Imagine teaching an AI the core values of an organization, not through explicit programming, but by simply letting it read a corporate document. This is the exciting promise of a new technique that aligns Large Language Models (LLMs) with the implicit and explicit values embedded within unstructured text. Traditionally, aligning AI values has required meticulous manual curation of data or specific rule sets. This new method bypasses that labor-intensive process by using a clever combination of synthetic data generation and fine-tuning algorithms. It starts by breaking down a document into smaller chunks and then employs a larger 'teacher' LLM to generate synthetic examples of both instructions and scenarios related to the values within each chunk. These synthetic examples act as training data, teaching the LLM to understand not just the *content* of the document, but also the underlying *values* it represents. The process involves two key stages: Supervised Fine-Tuning (SFT) and Preference Optimization (DPO). SFT uses the synthetic instruction data to guide the LLM in producing appropriate responses, while DPO utilizes the synthetic scenarios to further refine the LLM’s understanding of which actions are considered acceptable or unacceptable based on the given values. This approach was tested on two real-world examples: IBM's business conduct guidelines and the Universal Declaration of Human Rights. The results showed significant improvements in the LLM's ability to generate responses aligned with the document's values, outperforming traditional methods. Interestingly, adding retrieval mechanisms like those used in Retrieval-Augmented Generation (RAG) actually *decreased* performance, a surprising finding that highlights the delicate interplay between internal model knowledge and external data sources. This innovative technique opens doors to a new era of AI value alignment. Imagine effortlessly aligning LLMs with specific ethical guidelines, legal frameworks, or even individual preferences expressed in written form. While challenges remain in generating truly high-quality synthetic data and understanding the complex interplay of different AI mechanisms, this research marks a significant step toward making AI more responsible, adaptable, and truly aligned with human values.