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Natural Language Generation in Technical Writing

Natural Language Generation in Technical Writing

The use of Natural Language Generation (NLG) in technical writing has been gaining traction as a way to automate the creation of routine documentation. NLG leverages machine learning and artificial intelligence (AI) to automatically generate text based on structured data. This technology is particularly beneficial for technical writers who need to create large volumes of repetitive or standardized content, such as product manuals, API documentation, release notes, and FAQs.

In this article, we’ll explore how NLG works, its benefits in technical writing, and how it can be used to automate routine documentation processes.

What is Natural Language Generation?

Natural Language Generation (NLG) is a subset of AI that automatically creates human-readable text based on structured data inputs. Unlike traditional word processing, NLG systems generate unique content by analyzing patterns, instructions, and data sets. For technical writing, this means transforming complex data into clear and concise documentation, such as reports, guides, and user instructions, with minimal human intervention.

NLG differs from Natural Language Processing (NLP) in that NLP focuses on understanding and analyzing human language, while NLG focuses on creating it. By combining NLG with existing technical content workflows, organizations can streamline the production of routine documentation, freeing up writers to focus on more complex, high-value tasks.

How NLG Works in Technical Writing

In the technical writing space, NLG tools use a structured database—such as product information, user data, or code documentation—to produce text that aligns with predefined templates. The NLG system typically follows these key steps:

  1. Data Input: The system takes in structured data such as tables, XML files, or JSON objects.

  2. Content Planning: Based on predefined templates, the system decides the structure and outline of the document.

  3. Text Generation: Using algorithms and linguistic rules, the system generates human-readable text.

  4. Post-Processing: NLG tools can be programmed to check for grammar, style, and consistency, ensuring the output meets technical writing standards.

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Key Benefits of NLG in Technical Writing

Here are the reasons NLG is a good choice for technical writers

1. Efficiency and Speed

Automating routine documentation with NLG drastically reduces the time spent creating repetitive documents. Tasks such as updating software release notes, product specifications, or compliance reports can be automated, allowing technical writers to focus on complex, creative tasks.

  • Example: A software company can use NLG to automatically generate release notes by pulling information directly from product update databases. Instead of manually writing each note, NLG tools create a draft document based on data inputs like version numbers, updates, and bug fixes.

2. Consistency Across Documentation

NLG ensures that the tone, style, and terminology remain consistent across all documentation. This is crucial for maintaining a cohesive voice in large organizations where multiple technical writers contribute to the same documentation pool.

  • Example: A global hardware manufacturer could use NLG to produce installation guides in multiple languages while maintaining a consistent style and format across all regions.

3. Scalability

As businesses grow, so does the volume of technical documentation required. NLG allows organizations to scale their documentation efforts without needing to hire additional writers. The more structured data an organization has, the more content it can generate through automation.

  • Example: A telecom company could automate the production of detailed network reports for various customer segments, adapting the same templates for different regions or devices.

4. Reducing Human Error

Routine documentation often involves repetitive tasks that can lead to human error. NLG eliminates this risk by automating the generation of content based on accurate, structured data inputs.

  • Example: An API documentation system can use NLG to pull data from the codebase, reducing the risk of outdated or incorrect technical documentation by ensuring that any changes to the code are instantly reflected in the documentation.

NLG in Action: Practical Applications for Routine Documentation

Here are a few practical ways technical writers can use NLG for automating routine documentation tasks:

1. API Documentation

NLG can be used to automatically generate API documentation based on data from codebases, such as function descriptions, endpoint URLs, and usage examples. This allows technical writers to maintain up-to-date documentation without manually inputting every detail.

2. Product Specifications

For companies that manufacture products with detailed specifications, NLG can generate product descriptions, feature lists, and technical details by pulling data directly from product databases.

3. Release Notes and Change Logs

Software companies can use NLG to generate release notes and change logs based on version control system data. This automation ensures that every update is documented consistently and accurately, freeing up time for other tasks.

4. Compliance and Regulatory Reports

NLG can streamline the production of routine compliance documentation by pulling relevant data from databases and creating standardized reports that meet regulatory requirements.

Tools for NLG in Technical Writing

There are several NLG platforms and tools that can be integrated into technical writing workflows:

  • Arria NLG: A leading NLG platform, Arria allows users to automate the generation of reports and documents using structured data sources.

  • Wordsmith by Automated Insights: Wordsmith offers a robust platform for generating data-driven narratives and reports, useful for automating technical content like reports, product descriptions, and technical updates.

  • Contentful: While primarily a content management system, Contentful also supports NLG-based content automation workflows, particularly for technical documentation across multiple platforms.

Best Practices for Implementing NLG in Technical Writing

  1. Start with Structured Data: To make the most of NLG, ensure that your data is well-organized and structured. Data tables, XML files, or JSON objects provide the necessary input for NLG systems to generate meaningful text.

  2. Define Clear Templates: Successful NLG implementations depend on well-defined templates. Technical writers should collaborate with developers to create templates that match the company’s style guide and documentation needs.

  3. Human Oversight: While NLG can automate the bulk of routine content, human oversight is still essential. Writers should review the AI-generated text to ensure accuracy, clarity, and alignment with brand guidelines.

  4. Iterate and Improve: NLG systems learn from feedback. Continuously refine the templates and data inputs to improve the quality of the generated text over time.

NLG in Technical Writing

Natural Language Generation (NLG) is transforming the way technical writers approach routine documentation. By automating the creation of repetitive content, technical writing teams can boost efficiency, maintain consistency, and reduce the risk of human error. As NLG technology continues to evolve, its role in technical writing will likely expand, making it an essential tool for modern documentation teams.

Whether for API documentation, product manuals, or compliance reports, NLG offers a scalable and powerful solution to streamline the technical writing process. By embracing NLG, technical writers can focus more on high-level content creation, while routine tasks are handled by intelligent automation.

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