Enhancing Tech Documentation with Generative AI Tools

Technical documentation often presents a complex challenge for DevOps teams. Developers frequently express frustration with outdated or incomplete code documentation, and architectural diagrams, while visually appealing, may not accurately reflect the implemented systems. Even IT service management (ITSM) process flows for incident, request, and change management are inconsistently followed, highlighting a pervasive disconnect between documented procedures and actual practices.

Despite the insistence from technology leaders like CIOs and CTOs on comprehensive documentation, project budgets rarely allocate resources for dedicated technical writers. Agile development teams, focused on delivering new features, often limit their documentation efforts to code-level comments, README files, and other basic necessities. While product owners capture requirements in user stories, critical documentation—covering business rules, user journeys, architecture, APIs, and standard operating procedures—frequently remains incomplete or out-of-date. The growing use of generative AI (genAI) now prompts a crucial question: How can these advanced tools assist developers, engineers, and architects in writing and maintaining accurate and timely documentation?

Advancing Documentation with Generative AI

The inherent struggles with technical documentation have long been a source of contention within development circles. Some development teams have historically viewed documentation as superfluous, asserting that well-structured, thoroughly unit-tested code with high coverage and robust error handling provides all necessary insights. They argue that dedicating time to documenting existing features, at the expense of new development, is counterproductive, as these materials would quickly become obsolete with subsequent deployments. However, the advent of generative AI is fundamentally altering this perspective.

Proponents now suggest that genAI tools can empower DevOps teams to keep documentation current with the rapid pace of code changes and continuous deployments. Erik Troan, CTO of Pendo, highlights this shift, stating, “Generative AI is transforming the role of software documentation from static reference material into a dynamic layer within the product experience.” He adds that by automating the capture of user flows and generating contextual guidance, documentation can now evolve in real time alongside the software, significantly reducing friction and enhancing user efficiency.

Dominick Profico, CTO at Bridgenext, envisions a future where AI-generated knowledge could even supersede traditional documentation entirely. Profico suggests, “GenAI will enable us to reach a point where the comprehensive documentation leaders have long desired, and developers have consistently avoided, becomes obsolete.” He believes that large language models (LLMs) will advance to a level where documentation is truly dynamic, generated on demand in response to questions, chats, or prompts, drawing insights directly from the codebase, industry standards, existing documentation, support tickets, and system logs. Regardless of how the future unfolds, genAI is already proving invaluable in helping DevOps teams fulfill documentation requirements more efficiently. Moreover, the increasing integration of AI agents and other genAI development tools introduces new imperatives for investing in robust and accessible documentation.

Tailoring Documentation for Diverse Audiences

Before embarking on any documentation initiative, it is essential to clearly define the target audience and their specific needs. This foundational step establishes a baseline for what constitutes “good enough” and “up-to-date” documentation. Addressing the varied requirements of different stakeholders ensures that the effort invested in documentation yields maximum value. Each group interacts with the technical landscape in unique ways and therefore requires information presented in a format and level of detail that is relevant to their roles and responsibilities.

For newly onboarded developers, effective documentation should provide a comprehensive overview of the architecture, non-negotiable DevOps requirements, the standard software development process, and high-level code structure. This foundational knowledge helps them become productive quickly and ensures they produce solutions that align with established organizational standards. External development teams, such as those integrating with internal systems, require detailed API documentation, clear README files within Git repositories, precise data definitions in data catalogs, and guides on log files and other observability artifacts to facilitate seamless integration and troubleshooting.

Architects, security specialists, and site reliability engineers (SREs) depend on thorough documentation when recommending application modernizations, addressing technical debt, or responding to incidents and performing root-cause analysis. Their work demands deep insights into system design and operational characteristics. Data scientists, data governance specialists, and engineers building data pipelines often consume data generated by APIs and applications for reports, visualizations, analytics, and AI models. For them, updated data catalogs and clear data lineage information are crucial for data-driven decision-making.

Product managers, product owners, change leaders, and other subject matter experts (SMEs) need to understand “how the system works” without delving into code. They require more detail than typically found in release notes to grasp business logic and system behavior. Auditors, adhering to compliance standards such as ISO 27001, ISO 9001, SSDF, CMMI, and SOC 2, rely on readily available and accurate documentation to verify adherence to regulatory requirements. Finally, AI coding assistants and AI agents represent a new audience, consuming documentation to enhance their relevance and accuracy in assisting development tasks. Catering to these diverse needs ensures documentation serves as a versatile and valuable asset across the organization.

Leveraging Generative AI for Specific Documentation Types

Generative AI offers tailored solutions for a range of technical documentation needs, allowing teams to produce specific types of content efficiently. This targeted approach ensures that documentation is relevant and useful for its intended audience, from feature specifications to API details and operational procedures. Modern tools can automate much of the heavy lifting, making the documentation process less burdensome and more dynamic.

Documenting Feature Functionality

When it comes to detailing how features operate, tools can significantly simplify the process. Miles Ward, CTO at SADA, shares an impressive example, noting that Google Cloud’s API documentation, essentially “written in code,” demonstrates the only viable way to keep tens of thousands of API documents current is through automation. He explains that by ingesting technical documentation into tools like NotebookLM, users can obtain plain English explanations of nuanced feature interactions, even in podcast format. Ward emphasizes the rapid evolution of the state of the art, with new tools such as Gemini, NotebookLM, and Mariner aiding customers in transforming their documentation from a burdensome chore into a valuable asset.

To comprehensively document feature functionality, teams should consider maintaining: a feature specification detailing requirements, including end-user documentation; a concise technical design covering architecture, dependencies, testing, security, configuration, and deployment; and references linking to agile user stories and IT service management tickets. Effective tools for managing functional-level documentation include Microsoft Teams, Atlassian Confluence, Google Workspace, Notion, and MediaWiki.

Documenting APIs, Data Dictionaries, and Data Pipelines

For critical system components like APIs, data dictionaries, and data pipelines, generative AI can streamline creation and maintenance. Armando Franco, director of technology modernizations at TEKsystems, points out, “One of the most exciting shifts we’re seeing with genAI in the CTO office is how it transforms documentation from an afterthought into a natural byproduct of the development process itself.” He illustrates this by explaining that as teams develop microservices, genAI can automatically generate and maintain OpenAPI specifications that accurately reflect endpoints, payloads, and authentication methods. For data teams, AI can produce lineage diagrams and data catalogs directly from SQL code and ETL pipelines, ensuring consistency across various environments.

DevOps teams must remember that their primary audience for technical documentation extends beyond themselves, encompassing new developers joining the project or external teams utilizing APIs. Different types of technical documentation benefit from specialized tools: data dictionaries are best housed in data catalogs such as Alation, Atlan, Ataccama, AWS Glue Data Catalog, Azure Data Catalog, Collibra, Data.world, Erwin Data Catalog, Google Dataplex Universal Catalog, Informatica Enterprise Data Catalog, and Secoda. DataOps teams managing data pipelines and integration platforms can leverage visual design tools for clear data flow and lineage diagrams. For documenting APIs, popular choices include Postman, Redocly, Swagger, and Stoplight.

Documenting Runtime Environments and Standard Operating Procedures

Maintaining up-to-date documentation for runtime environments and standard operating procedures is crucial for operational efficiency and system stability. Kevin Cochrane, CMO at Vultr, observes that “Traditional documentation practices haven’t kept pace with the dynamic, real-time nature of today’s AI-driven cloud systems.” He highlights that CTOs are now employing genAI tools to convert logs, configurations, and runtime data into “living documentation” that evolves with the system. This approach helps teams reduce friction, accelerate development, and transforms documentation into a continuity tool, preserving shared context, mitigating single points of failure, and preventing execution breakdowns across the entire technology stack.

DevOps best practices emphasize workflow, tools, and configuration, often leaving teams to define how to document handoffs between development and operational functions. Various tools can fill these gaps: operational knowledge bases and standard operating procedures can be managed with Atlassian Jira Service Manager, Freshservice Knowledge Base, ServiceNow Knowledge Management, and Zendesk Guide. AI-powered log-file analysis tools like Datadog, Dynatrace, LogicMonitor, Logz.io, New Relic, Splunk, and Sumo Logic offer deep insights. Tools for visualizing public cloud infrastructure include Cloudcraft, Hava, and Lucidscale, while general diagramming tools such as Draw.io, Figma, Eraser, Lucidchart, Miro, and Visio are essential for architecture and sequence flows.

Enabling AI Agents and Addressing Legacy Systems

Generative AI’s utility extends beyond human-readable documentation, also serving as a critical resource for AI agents and providing a powerful mechanism for tackling the challenges of legacy system documentation. This dual benefit underscores the transformative potential of AI in knowledge management.

Empowering AI Agents with Comprehensive Documentation

A growing number of code-generating AI agents are now capable of analyzing software documentation in addition to codebases, providing them with crucial contextual understanding. Andrew Filev, CEO and founder of Zencoder, notes, “When every code change is documented, AI agents can understand not just what the code does, but why it was written that way, and this historical context transforms AI from a coding assistant to a knowledgeable team member.” He emphasizes that this institutional knowledge, often previously confined to developers’ minds or scattered across communication channels, becomes searchable and actionable intelligence, improving every subsequent AI interaction.

DevOps teams should strategically feed AI code-generators with comprehensive documentation including APIs, user stories with acceptance criteria, coding standards, architecture principles, README files, secure coding guidelines, data privacy rules, and compliance references. Filev further elaborates, “LLMs perform three times better with detailed documentation because they can understand context, constraints, and intentions.” Teams adopting this approach have reported that after just six months, their AI agents become dramatically more effective at understanding their specific codebase patterns and conventions.

Documenting Legacy Applications with AI Capabilities

One significant application of generative AI is in documenting legacy applications, especially when original developers are no longer available. Sanjay Gidwani, COO of Copado, outlines three key AI capabilities that simplify the documentation of existing systems: First, genAI excels at summarizing vast amounts of material, allowing it to efficiently read existing source code and distill its intent. Second, many business application systems rely on configuration metadata, and AI with metadata awareness can effectively read and document these configurations. Third, AI can analyze system data to deduce actual processes, complete with stage durations and participant identities.

While undocumented systems pose significant problems, including potential compliance issues, creating excessively verbose documentation also presents challenges. Long-form documentation is difficult for humans to consume and costly to maintain, even with AI assistance. The most effective strategy is to keep the target audience in mind and maintain only “just enough” documentation. All documentation efforts, whether for human reviewers or for LLMs, should be precisely targeted to ensure relevance, usability, and maintainability.