Reimagine software documentation by introducing AI

Updating software without proper documentation is like traveling to somewhere new without good directions. It’s easy to get lost. There’s a reason thorough documentation is a hallmark of software development.

Software documentation can be found in technical manuals and accessed via online resources. For those modernizing applications, it contains vital information about and descriptions of your app. It’s a necessity for any organization that develops apps and a major asset to those who use one.

Trying to navigate software projects with outdated or incomplete documentation creates confusion, slows progress and exposes projects to unnecessary risk. Effective documentation is an often-overlooked component of successful application modernization, but even more critical as organizations push forward with these initiatives. However, it can be a time-consuming manual process.

The good news is that AI-driven solutions, particularly generative AI, can boost software developer productivity in several areas. This includes streamlining code documentation, making each step much faster for developers. With structured prompts (more on those later), you can use AI to tackle four common documentation tasks. In doing so, you can improve efficiency, save time and ensure consistency in your documentation.

#1: Generate documents automatically

Documentation is a baton passed from one developer to the next, and it must be current, accurate, and thorough if you want to make progress and avoid major errors. Documentation is valuable because people change roles and move to other projects, and you can lose their institutional knowledge of the software. Documentation is a gift to the next person responsible for software updates.

Imagine your developers free from the time-consuming task of manually crafting technical documentation. Such freedom could allow them  more time to develop new features that increase user satisfaction. Generative AI models can craft essential documentation by analyzing code repositories, design diagrams, required formatting, existing artifacts and other specifications. 

Using AI in this way can increase the accuracy of the documentation and make the process more efficient through intelligent automation. It also could make your documentation more consistent across the organization.

#2: Review documents for quality assurance

Document reviews act as a quality check. A manager or a subject-matter expert reviews the documentation and offers feedback to the developer. These reviews increase the quality of your documentation and reduce the risks associated with outdated or incorrect information, but they’re tough to manage manually.

AI can be used to expedite reviews and make the process more objective. This gives human experts more time for strategic quality evaluation. Input structured prompts that define review objectives, evaluation criteria and expected feedback. The AI tool examines documents against industry best practices, company standards and stakeholder feedback to find areas for improvement.

AI-powered reviews can:

• Analyze documents for clarity, accuracy and project goal alignment.
• Catch errors, inaccuracies and areas needing clarification so you can fix them before publishing.
• Inject efficiency and impartiality into the review process.

While generative AI offers a powerful tool for documentation, it doesn’t replace human expertise. A thorough human review is always recommended to verify accuracy, add context and ensure the documentation aligns with your specific needs.

#3: Revise documents to address feedback

Reviews inform you of what needs to be changed but document revisions make these changes a reality. Your documentation should reflect the latest project updates, brand guidelines and industry regulations. Incorporating feedback seamlessly is a developer objective that can be achieved by introducing AI.

Gen AI models can produce efficient document revisions based on structured prompts. These prompts incorporate annotated documents with reviewer notes or change requests, directing the AI to make the necessary revisions. Doing so helps ensure updates accurately reflect feedback and maintain consistency across documentation sets, saving time and promoting alignment within modernization efforts.

AI-driven revisions can be performed at scale, allowing for swift updates across large volumes of documentation, which is especially beneficial in complex modernization projects.

#4: Extract key insights rapidly

Business decision-makers need to deeply understand trends, issues and opportunities to advance initiatives that propel the company forward. This includes the insights contained in your software documentation, but it can be quite technical. And decision-makers probably don’t have time to dig through hundreds of pages of documentation to discover insights.

AI models serve as a powerful tool in this context, converting technical information into easily digestible insights. Use AI to summarize lengthy documents, complex project plans or research papers associated with modernization initiatives.

Such capabilities save decision-makers significant time and help them quickly identify potential roadblocks or areas of improvement. It does this by delivering concise summaries that highlight key insights and findings that facilitate informed decision-making throughout the application modernization process. It could also pay off in instances such as training and when comparing the features of different applications.

Optimizing the power of the prompt

Many organizations seek prime opportunities to deploy AI. However, to capitalize on these opportunities, structured prompts are a necessity. These prompts act as recipes do for cooking, guiding AI tools toward desired outcomes. Just as the quality of a dish improves with a better recipe, the outputs you receive from AI correlates to the quality of the inputs you provide. If the input is subpar, the resulting outputs will inevitably fall short of expectations.

Carefully crafting structured prompts maximizes AI's effectiveness with documentation tasks, like analysis, feedback and revisions. Well-written prompts significantly improve documentation quality and efficiency.

While there’s still industry debate about prompt standards, you can make your prompts more effective. By giving clear and specific guidance through details and context, the AI models can perform requested tasks better, which leads to better-quality results or outcomes. While AI can speed your processes, take care and don’t rush when writing your initial prompts. And keep experimenting as you write future prompts to finesse your approach.

Explore how AI can support software development

In addition to documentation, testing is another application of generative AI to support the software development process. But there are plenty more. Unisys can assist you in exploring these opportunities and identifying the optimal AI solutions to propel your organization forward.