Using LLMs to refactor outdated documentation

Refactoring outdated documentation using Large Language Models (LLMs) is a powerful strategy to improve clarity, consistency, and accuracy, ensuring that content remains relevant and user-friendly. LLMs can assist in various stages of this process, from identifying obsolete content to rewriting and organizing text for modern readers. Here’s how LLMs can be applied effectively to update documentation.

1. Identifying Outdated Information

The first challenge with outdated documentation is pinpointing which sections need updating. LLMs can help in several ways:

• Keyword and Topic Analysis: By feeding the existing documentation into an LLM, you can ask the model to identify terms or concepts that have evolved, become obsolete, or are no longer accurate. The model can suggest areas where terminology may have changed or where newer technologies or practices supersede old methods.

• Version Comparison: If you have multiple versions of documentation, an LLM can compare them to spot differences and highlight areas where updates have been made or missed. This process can help uncover sections that haven’t been updated in a while.

2. Improving Clarity and Readability

Outdated documentation can often suffer from unclear language, excessive jargon, or poor structure. LLMs can assist with:

• Rewriting for Simplicity: LLMs can rephrase complex sentences or technical jargon into simpler language, making documentation more accessible to a broader audience, including new users or non-experts.

• Grammar and Style Consistency: LLMs can analyze the text for consistency in tone, formatting, and style, ensuring the entire document follows a standard guide or set of guidelines, which is crucial for corporate documentation.

3. Standardizing Terminology and Formats

In many cases, outdated documentation might use inconsistent terminology or formatting. Using LLMs, you can:

• Consistency Check: Ask the model to review terminology across the document and standardize the use of specific words, phrases, or technical terms.

• Document Structure: LLMs can suggest better ways to structure the document, including creating consistent headings, subheadings, bullet points, or numbering systems. This helps users navigate the content more easily.

4. Updating Technical Content

Technical documentation often becomes outdated because of rapidly changing technologies, practices, and tools. LLMs can:

• Research and Fact-checking: By querying the model with updated information, you can confirm whether current technical practices or features mentioned in the documentation still hold true. LLMs can also assist in inserting more up-to-date examples, code snippets, or API references.

• Updating Examples: LLMs can generate new code samples, use cases, or examples based on the latest industry standards, helping to keep the documentation current.

5. Generating New Sections

In some cases, outdated documentation might be missing important sections, or it may lack critical updates. For instance:

• New Features or Capabilities: LLMs can be instructed to generate content that addresses newly added features, functionality, or tools that were not present in earlier versions of the documentation.

• Best Practices: As best practices evolve, LLMs can be tasked with adding up-to-date guidance, recommendations, or checklists to ensure the documentation is aligned with current industry standards.

6. Maintaining the Human Touch

While LLMs can assist with the technical aspects of refactoring documentation, there’s often a need to retain the human element in content creation. LLMs can:

• Personalization: Tailor the language and tone of the documentation to fit the organization’s voice, making sure it aligns with existing communications.

• Feedback Loop: Incorporate feedback from subject-matter experts (SMEs) or team members to ensure accuracy and nuance are maintained in the refactored documentation.

7. Scaling Documentation Refactoring Efforts

One of the main benefits of using LLMs for refactoring outdated documentation is scalability. Large volumes of outdated content can be processed quickly, ensuring that refactoring efforts aren’t limited by time or manpower constraints. By automating parts of the process, LLMs can free up resources for more strategic tasks, such as reviewing complex sections or approving final versions of the documentation.

8. Ensuring Accessibility

Finally, outdated documentation may not be fully accessible to all users, especially those with disabilities. LLMs can be used to:

• Ensure Compliance: Ensure the updated documentation adheres to accessibility standards such as WCAG (Web Content Accessibility Guidelines).

• Generate Alternative Text: For visual content such as diagrams or images, LLMs can generate descriptive alternative text, making the documentation more accessible to screen readers.

9. Continuous Updates

Once the documentation is refactored, LLMs can help maintain it over time. Instead of relying on manual updates, LLMs can be programmed to:

• Monitor Industry Changes: Periodically scan new content or external sources to flag any new developments that may require updates to the documentation.

• Automated Revisions: Set up automated workflows where LLMs periodically propose updates to documentation based on new standards or practices.

Conclusion

LLMs provide a versatile and efficient way to refactor outdated documentation, ensuring it remains relevant, clear, and accessible. By leveraging AI to automate the identification of outdated content, improve readability, standardize terminology, and integrate current best practices, organizations can keep their documentation up-to-date with minimal manual intervention. Additionally, the scalability of LLMs makes them a great solution for large documentation repositories, reducing the resource burden on documentation teams.