Creating internal glossaries using foundation models

Creating internal glossaries using foundation models, such as GPT or other large language models (LLMs), is an efficient way to automate and streamline knowledge management within organizations or projects. A well-structured internal glossary helps employees quickly understand jargon, technical terms, abbreviations, and other specialized language that may evolve or vary over time.

Here’s a structured approach to creating an internal glossary using foundation models:

1. Data Collection: Define Scope and Gather Terms

• Identify Relevant Terms: The first step in creating a glossary is identifying which terms to include. Depending on your industry or organization, this could be highly specialized, like terms used in finance, healthcare, law, or software development.

• Automated Term Extraction: Use a foundation model to scan internal documents, emails, manuals, or other knowledge sources to identify commonly used terms or phrases that should be defined.

• Crowdsourced Feedback: You can also ask employees or domain experts to contribute terms they feel should be included.

2. Generating Definitions with Foundation Models

Once the terms are collected, use a foundation model to generate or suggest definitions. Here’s how:

• Automated Definition Generation: Input the term into the LLM and ask it to generate definitions. For example: “Define the term ‘blockchain’ in a simple yet detailed manner for a non-technical audience.”

• Contextual Understanding: Foundation models can generate definitions by understanding the context in which the term is used, ensuring that the definition is relevant to your organization’s specific needs.

• Disambiguation: Some terms might have multiple meanings depending on the context. A foundation model can help by providing alternative definitions for different uses of the same term, which can be tagged for clarity.

3. Refining the Definitions

• Review and Refinement: While LLMs can generate definitions, it’s crucial to have a human expert review them for accuracy, clarity, and relevance to the organization.

• Personalization: Customize the definitions to reflect how your organization uses a term. For instance, a term like “API” in general tech might have a very specific meaning in the context of your product or service.

• Simplification: For broader accessibility, definitions should be clear, concise, and free from unnecessary technical jargon. You can use a foundation model to simplify definitions if needed.

4. Organizing the Glossary

• Alphabetical Index: Organize terms alphabetically or by category (e.g., technical terms, process-related terms, product-specific terms). LLMs can help create this structure automatically.

• Categorization: Include categories to group related terms together, making the glossary more intuitive. Foundation models can be trained to suggest suitable categories based on the terms.

• Cross-References: Use the model to create cross-references between related terms within the glossary. For example, if “API” is mentioned in the definition of “webhooks,” it could be hyperlinked to the “API” entry.

5. Automating Glossary Updates

• Real-Time Updates: As new terms emerge or existing ones evolve, foundation models can continuously process internal communications (e.g., new projects, updates, or reports) to automatically flag new terms and suggest updates to existing ones.

• Version Control: Implement version control for the glossary to keep track of updates and changes, ensuring that outdated or incorrect definitions are addressed promptly.

6. Providing Accessibility and Integration

• Searchable Database: Use the glossary as a searchable resource, integrated into your internal knowledge management system (e.g., SharePoint, Confluence, or custom-built tools). Foundation models can help structure this integration.

• Natural Language Queries: Implement a feature that allows employees to ask natural language questions about terms. For example, a user might ask, “What does ‘API’ mean?” and the model could retrieve the definition.

• User-Specific Personalization: Advanced models can even adapt definitions based on a user’s role or expertise level, providing more technical explanations for developers and simplified versions for new employees or non-technical staff.

7. Using the Glossary for Training and Onboarding

• New Employee Onboarding: A well-curated glossary is a valuable tool for onboarding new employees. You can integrate the glossary into training modules or learning management systems (LMS) to help new hires get up to speed with company-specific terminology.

• Continuous Learning: Encourage ongoing usage of the glossary as a learning resource. With the right integration, employees can easily reference terms during their day-to-day tasks.

8. Enhancing the Glossary with AI and Feedback Loops

• User Feedback: After employees use the glossary, provide mechanisms for them to suggest improvements or report unclear definitions. Foundation models can process this feedback to refine the glossary automatically.

• AI-Assisted Refinement: Over time, foundation models can learn from the feedback and improve its suggestions. For example, if a user frequently asks for clarification about a definition, the model can suggest changes to improve clarity.

9. Compliance and Data Privacy Considerations

• Confidential Terms: Make sure that sensitive or proprietary terms are flagged and handled appropriately. Foundation models can be trained to distinguish between confidential and public terms, ensuring that the glossary remains in compliance with legal and data privacy standards.

• Data Governance: Maintain robust data governance practices to protect against any unintentional sharing of internal knowledge or proprietary terminology.

10. Evaluating Effectiveness

• Measure Usage: Track how often employees refer to the glossary and whether they are satisfied with the definitions. This feedback can help assess whether the glossary is improving communication and knowledge sharing.

• Continuously Improve: Foundation models can assist in identifying which terms are most frequently searched for, providing an opportunity to continuously refine the glossary to keep it relevant.

Conclusion

Building an internal glossary with foundation models can significantly improve efficiency, knowledge sharing, and collaboration across an organization. By using AI for term generation, refinement, and continuous updates, businesses can maintain a dynamic and accurate glossary that supports the evolving needs of their teams. With well-organized, accessible definitions, employees can focus more on their work and less on deciphering jargon or unclear terminology.