Organizations can generate targeted glossaries by having models scan internal records such as emails, transcripts, and codebases to surface unfamiliar terms. Human domain experts subsequently review and correct these dictionary-style definitions before storing them in version control. Storing these assets within distinct bounded contexts minimizes token overhead and helps distinguish between internal jargon and customer-facing language. In agentic engineering workflows, these curated terms guide automated code generation while automated sensors continually verify interface compliance.
Key points
Creating separate glossaries for distinct bounded contexts prevents confusion across diff…: Creating separate glossaries for distinct bounded contexts prevents confusion across different software codebases and departments.
Contextual glossaries prevent proprietary or deprecated internal jargon from appearing wi…: Contextual glossaries prevent proprietary or deprecated internal jargon from appearing within customer-facing user interfaces.
Automated sensors and scheduled reviews ensure code generation matches naming conventions…: Automated sensors and scheduled reviews ensure code generation matches naming conventions as enterprise language evolves.
Sources checked by our analysis pipeline: 1
Generated Glossary
12 November 2026
When we use LLMs in a specialized domain, such as the operations of any reasonably-large organization, there is a lot of specialized vocabulary. Such words and specialized meanings will be foreign to an LLM because they aren't in its training set, making it harder for the LLM to be effective when doing tasks in that domain. One technique to aid this is to use an LLM to generate a glossary of the terminology in that domain, and make this glossary available for any LLMs doing work in those circumstances.
The sources for the LLM can be any material within the organization concerned such as documents, emails, and chat logs. In some cases getting transcripts of audio or video may be helpful, although most organizations generate enough text to do the job.
The glossary-generating LLM should look for single words, and phrases of several words that regularly hang out together to form a meaningful term. The generator should be asked to identify terms that are new to it, but also known terms that are being used…
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