Bad Documentation is Just as Harmful as Bad Code

Maybe your business has felt the impact of this firsthand. A key developer leaves, and suddenly simple updates turn into multi-day tasks with time wasted reinventing the wheel.

Why Traditional Documentation Fails

Research shows that developers spend nearly a quarter of their working time searching for context within their code and more than half of those surveyed spent over 30 minutes a day just troubleshooting undocumented code.

It’s an unfortunate reality that documentation is one of the first things to be sacrificed when deadlines are tight and the pressure is on to ship new features faster. Even when time is set aside for documentation, the moment it’s written, it’s already out of date.

Code is constantly evolving, but documentation rarely keeps pace. Updating it manually is time-consuming and often deprioritized, which leads to knowledge gaps that slow teams down.

When documentation falls behind, teams lose tacit knowledge when key developers leave, experience longer resolution times when debugging issues and slower onboarding of new team members.

This is why traditional documentation has been an uphill battle. That is, until now.

A Smarter Way: Intelligent Documentation Powered By AI

Rather than treating documentation as an afterthought, we embed it directly into our development workflow. We leverage AI to ensure documentation stays in sync with the codebase, enabling developers to interact dynamically with their technical knowledge using AI-powered IDEs like Cursor.

When the code changes, documentation updates automatically. No more stale docs. And our team can query the system with practical questions like: How does this API handle cancellations? Where do we update dynamic pricing logic?

This means new developers get up to speed faster, while support teams spend less time hunting for answers and more time solving problems.

Building Documentation Into the Codebase, Not a Separate System

Traditionally, our documentation lived in external systems like Confluence which required manual upkeep. It led to outdated information and context gaps.

Our new approach solves this by making documentation a first-class citizen in the codebase itself.

Within our IDE, every project contains two key folders:

/TechnicalDocumentation

Auto-generated Markdown files derived from code analysis, including API specifications, architecture diagrams, and data flow documentation. All the artifacts that used to live in Confluence now live alongside the code.

/AI

A repository of prompt histories, change logs, and context models. This ensures documentation remains increasingly relevant over time instead of becoming outdated.

Our system continuously tracks Git commits and updates documentation in real time. We use vector databases to link documentation directly to relevant sections of code and cross-reference generated content against tests and CI/CD outputs to flag discrepancies before they become issues.

Decoding Legacy Systems: Faster, Smarter Reverse Engineering

This approach doesn’t only benefit the ongoing development of new code. Rebuilding a system from an inherited codebase is another common use case and one that is often like unraveling a tangled web—especially when there’s little to no documentation.

In the recent redevelopment of a financial reporting system, the previous team had left behind a complex financial data model, with obscure field names, duplicated tables, and business logic scattered across an aging database and frontend scripts. Traditionally, making sense of this would have taken us weeks, if not months, requiring us to manually inspect SQL queries, trace UI elements, and piece together logic through trial and error.

With an AI-driven approach, we dramatically cut down this onboarding time. By feeding AI raw database exports, UI screenshots, and code snippets we could identify patterns, infer relationships between entities, and even generate missing documentation. AI not only mapped out the underlying structure but helped validate assumptions, highlight inconsistencies, and suggest improvements to streamline the new system’s design. Instead of spending countless hours manually deciphering legacy logic, our team could pinpoint how calculations were handled, what fields were critical, and where discrepancies existed between the existing system and the redesigned one, in real-time as designs were being developed.

This process wasn’t just about documentation but about advancing our understanding of a live system, in record time. What used to be a painstaking, manual process of reverse-engineering was accelerated, allowing us to confidently align the new system with existing logic while improving its architecture for future scalability.

Scalability as Standard

Most agencies focus on delivering a good-looking, functional product but few are truly set up to help you use, scale, and evolve your solution.

Agencies that embrace AI in their workflows benefit from no more knowledge loss, less time wasted "figuring things out” which means faster updates and fixes, and smoother handovers between teams.