Have you read The Three-Body Problem? In it, the alien Trisolarans construct a massive coordinated computer system using millions of humans (well, Trisolarans, technically) to meticulously perform manual calculations. Whenever I see large engineering teams trying desperately to keep technical documentation current and comprehensive, I’m reminded of that “human formation computer.” The immense effort, coordination, and meticulous attention to detail required to manually maintain documentation at scale are staggering—and unsustainable.

The core problem with managing technology at scale is complexity. As engineering teams grow, so does the number of interconnected parts, making it challenging to fully understand, analyze, predict, and manage. This impacts every part of team collaboration, from onboarding new engineers to shipping new features to explaining to your customers how your software interfaces work.

Well, now we can finally do something about it. At Driver, we believe that accurate, exhaustive, and automatically up-to-date documentation is the key to managing complexity. To make sure we’re talking about the same thing, here are some of the things we consider technical documentation:

• Exhaustive symbol-level documentation for every codebase.

• System-level documentation showing codebase structure, data flow patterns, and inter-system dependencies.

• Customer-facing documentation including API references, getting started guides, tutorials, and overviews.

• Comprehensive hardware reference manuals for heterogeneous embedded systems.

• Regulatory documentation tracing decision-making processes in sensitive systems like automated trading platforms.

• … and more.

It’s impossible to solve for accurate, up-to-date documentation at scale with humans for three key reasons. First, technical documentation requires ongoing manual effort from experts. Second, docs cannot be systematically created or validated. Finally, everything has to be updated manually as code changes constantly. As a result, critical information is out of date, hard to find, and spread across silos—leading to bottlenecks, slower onboarding, miscommunication, and frustrated customers.

Driver simplifies documentation complexity with AI. We approach the challenge differently: starting from the source code and systematically building upward. In this article, we’ll explain why comprehensive, accurate, and up-to-date technical documentation is foundational to managing complexity—and why it’s fundamentally impossible to achieve with human effort alone (”human formation documentation,” if you will). We’ll walk through how Driver automates symbol-level docs from source code, builds layered documentation on top, integrates with CI/CD workflows, and transforms documentation from an afterthought into a real-time system of record. Along the way, we’ll share how we define and measure documentation quality, why automation changes the economics of documentation, and how it all adds up to faster onboarding, clearer architecture, better support, and happier teams.

Exhaustive Symbol-Level Documentation

Symbol-level documentation forms the foundation of technical documentation, providing definitions, parameters, and basic descriptions for all code elements (functions, classes, modules, etc.). Today, developers have to write the symbol-level documentation themselves. Available tools in this domain, like Doxygen and Javadoc, are built to organize information and render this manually generated content. If the developers don’t write the documentation, you don’t have any, and when they do, organizations have little control over consistency, quality, and accuracy. This leads to sparse, incomplete documentation with critical gaps.

Driver’s fundamentally different approach is to introduce automatic, exhaustive generation of symbol documentation for the entire codebase. This new capability is possible only with the advent of LLM technologies. But, importantly, we leverage static analysis to programmatically parse all symbols and then apply LLMs in a constrained, granular manner to create documentation for each symbol in a consistent and accurate format. This exhaustive foundation is the cornerstone of our approach and enables automatic synthesis of other kinds of documentation on top, which we’ll discuss next, as part of secondary (or more) passes.

Consistency is another major challenge for the human approach. Multiple developers may write documentation in different styles, leading to a patchwork of formats and terminologies. Driver solves this by applying a consistent format and style across the entire codebase, making navigation seamless and intuitive. Or, if you want to change the format, perhaps to apply a standard formatting guideline (e.g., Simplified Technical English), that’s also no problem. Brauchen Sie es stattdessen auf Deutsch? Auch das ist kein Problem.

Finally, a human approach tends to treat each symbol in isolation, making it hard to understand how components interact. Driver changes that by automatically discovering and documenting relationships—like function calls and usages, inheritance hierarchies, and data flows. This interconnected view transforms isolated symbols into a navigable knowledge graph, giving developers a clear understanding of the broader architecture.

Driver redefines what quality looks like in documentation by providing real-time, code-accurate, and fully connected knowledge bases. Here’s how we make this measurable:

• Documentation Coverage & Completeness: The percentage of symbols (functions, classes, modules, etc.) that are fully documented with all necessary information (purpose, parameters, examples, edge cases) and the completeness of technical procedures and system descriptions.

• Technical Accuracy: How correct and error-free the documentation is, including the accuracy of code examples, function names, and parameters in the documentation.

• Depth & Conciseness: The ability to provide appropriate detail levels for each documented element, ensuring sufficient understanding while avoiding information overload.

• Consistency and Style: The uniformity of documentation tone, format, and structure across the codebase. Consistency is harder to measure numerically, but it impacts user experience.

• Intelligent Context: The ability to automatically discover and surface relevant connections within the documentation, such as cross-references made between components and relationships between different parts of the system.

Documentation, Automated

Having a complete, accurate set of documentation up-to-date for any one commit is hard. Keeping it that way at every commit is damn near impossible. Driver provides the fundamental solve by automating content generation and delivers this by integrating with development workflows, making documentation part of the software lifecycle rather than a separate activity:

• Documentation updates become part of the CI/CD pipeline

• Pull requests automatically include documentation changes

• Developers can review required documentation updates alongside code reviews

• Documentation quality becomes a measurable component of release readiness

This eliminates the historical disconnect between development and documentation, ensuring documentation accuracy without requiring additional processes or team members.

Instead of leaving documentation to the end — the caboose in your delivery process — it can be an integral part of the software delivery lifecycle. Here’s how we make this measurable:

• Maintainability & Freshness: How easily the documentation stays up-to-date as the code evolves, quantified by tracking documentation “drift” – for example, measuring the time lag between a code change and the corresponding doc update.

• Authoring Effort: The human effort (time, cost) required to create and maintain the docs, such as hours developers or technical writers spend writing documentation, as well as ongoing maintenance efforts.

Writing high-quality documentation manually is time-consuming, often eating up as much as 30% of a developer’s time. Driver changes this dynamic by auto-generating updates directly from changes to your code, freeing up thousands of hours for core development work.

Business Impacts

To successfully drive business outcomes, documentation needs to be discoverable where the end-users, both internal and external, are looking for it. For engineers day-to-day, this means viewing tech docs and interacting with chat in their integrated development environment (IDE), using local development tools to apply Driver to fresh code, and controlling how documentation gets exported. For end-users, this means a documentation portal organized into a narrative they expect with dead-simple navigation and discoverability.

Driver makes it easy to integrate documentation anywhere it’s required. Here’s how we measure these:

• User Adoption & Satisfaction: How effectively the documentation is used by developers. Metrics include documentation page views, search queries, chat response success rates, and feedback scores.

• Onboarding Efficiency: Measures the time and success rate of new employee or new customer onboarding through A/B testing. Tracks metrics like time-to-first-commit, number of questions asked, and completion rate of onboarding tasks.

• Developer Productivity Impact: Tracks and compares key productivity metrics before and after documentation system implementation, including: average time spent writing/updating documentation per sprint, time spent searching for technical information, number of inter-team clarification meetings needed, and velocity of feature development.

• Support Resolution Time: Measures and compares the time taken to resolve customer support tickets and technical issues, and the number of escalations needed.

Driver transforms documentation into a true business enabler, cutting down ramp-up times, accelerating development, and improving support outcomes. We like to think of our documentation as active instead of passive. It doesn’t just sit there—it helps drive your business forward.

Beyond Symbol-Level Docs

Driver opens the door to documentation that was rarely written manually because of effort constraints, including comprehensive internal documentation for large legacy systems, in-depth architecture analyses, or combined knowledge bases that answer natural language queries.

For example, documenting an entire legacy codebase of tens of millions of lines (including generating high-level summaries of each module, listing all dependencies, etc.) would be a monumental task for a human team, so it often isn’t done at all. Driver can tackle this systematically: Akin to secondary passes in compilers, our approach prioritizes exhaustiveness and building on top of understanding resolved at lower levels of abstraction, which are the key ingredients to building this kind of content.

Another example is multi-language, unified documentation: Driver can generate documentation for multiple programming languages in one project (useful for polyglot codebases), something that is manually very costly. Driver can also produce dynamic diagrams or do on-the-fly analyses (like “what calls this function?” queries) that aren’t part of traditional docs. These novel outputs drive value by improving understanding and reducing knowledge silos.

We enable this with a documentation-as-code approach, taking inspiration from infrastructure-as-code. Just like Terraform allows you to express infrastructure requirements in code, Driver lets you define documentation requirements programmatically, ensuring consistency and automation across your entire documentation lifecycle. Here are some novel ways this approach is being used:

• Tutorials and onboarding flows

• Architecture documents and system overviews

• Release notes and migration guides

• New feature implementation guides

• Hardware reference manuals for embedded systems

• Data flow and decision making analysis

These documents reference the symbols level documentation directly, synthesizing the information for the intended audience. This is accomplished through a configuration file that specifies the audience, the structure of the document (sections and subsections), and the objectives for each section. Driver then compiles the configuration to produce the document. These configuration files can be tracked alongside your codebase allowing updates to be integrated into the CI/CD process.

Full Visibility Into Your Tech Stack

Driver is redefining the impact great technical documentation can have on engineering teams and an organization at large. Unlike traditional approaches that rely heavily on manual input and struggle with scalability, Driver leverages AI to create living, breathing documentation that evolves with your codebase. Through automation, Driver makes it possible to achieve near-complete coverage, even for large and complex systems. This shift from static documentation to dynamic, real-time updates means your knowledge base is always in sync with your code, accessible across teams, and ready for any scale.

This flexibility, combined with integrations into CI/CD pipelines, ensures that documentation is continuously updated as part of your development process—not as an afterthought. By reducing the time developers spend on documentation by up to 90%, Driver not only cuts costs but also enhances productivity. With Driver, documentation becomes more than just a record—it’s a shared understanding that drives smarter decisions, faster development, and better support. With Driver, you’re not just documenting your technology—you’re elevating how your organization understands and builds it.