Specialized vocabulary in large organizations poses a challenge for Large Language Models (LLMs) due to their absence in training sets. To address this, LLMs can generate domain-specific glossaries from organizational documents, emails, and chat logs. This process involves identifying novel terms and existing terms used in new ways.Once identified, an LLM can create definitions, noting contextual variations for terms with multiple meanings. Human experts must then review and refine these draft glossaries. The finalized glossaries should be accessible to LLMs in an efficient format, minimizing token consumption for lookups.Regular review and refreshing by both humans and LLMs are crucial, and version control should be maintained. Organizations may have subtly different definitions across various bounded contexts, such as source code. Separate glossaries for each bounded context are often more effective than a single, standardized one.Comparing glossaries from different contexts can highlight usage variations. For large glossaries, including only terms and links to definitions initially can optimize token usage. Glossaries can guide code generation and ensure internal terminology is not exposed externally in customer interfaces.Including deprecated terms with suggested replacements and managing their usage over time is also beneficial. The difficulty of specialized language in large organizations is a common theme, making LLM-generated glossaries a practical step in domain modeling and context layering.