The Unsung Role of Documentation
You might think, “Why does documentation matter if the code works?” Well, herein lies the issue: undocumented code is like a map without labels. Sure, you might find your way eventually, but it’s going to be slow, error-prone, and expensive.
Proper documentation helps:
• New developers come on board more quickly
• Stakeholders view systems as logical constructs.
• QA teams test more efficiently.
• Your team are able to update features without introducing bugs.
• Avoid vendor lock-in or key-person risk
When documentation is lacking, teams spend hours or days deciphering code, retracing decisions, and reverse – engineering logic. That means higher costs and slower development cycles-something no startup can afford.
Introduction
AI can generate code, but it can scaffold an MVP and even refactor logic and optimize functions. The one thing it can’t do, at least not well, is write documentation. And in today’s software development landscape, that’s a massive oversight with long-term consequences.
At Appricotsoft, we’ve worked with dozens of AI-generated projects and run technical audits for startups across Europe and the U.S. There’s one red flag that we find again and again: zero internal documentation, or near enough. And no, auto-generated comments from Copilot don’t count.
In this post, we’ll break down why documentation is still critical-even in the age of AI-and what happens when your AI-driven development process skips it.
AI misses out the comments, context, and clarity.
AI tools like GitHub Copilot and ChatGPT are phenomenal at generating code, but they fundamentally lack one key thing: context. AI doesn’t know why you made a certain architectural choice. It doesn’t understand long-term product goals, business logic, or the nuances of your user base. As a result:
• It can’t explain why something was done.
• It does not log architectural trade-offs or product decisions.
• It doesn’t capture the team discussions or implementation options that were considered and rejected.
The result? You get output without rationale. Code without conversation. A structure without story.
Later, when the founders bring in new developers or external code audit services, what they get is a black box. That’s where we come in: due diligence technical audits at Appricotsoft often commence with rebuilding the missing narrative behind AI-generated features.
It thus appears by definition that all verbs mean action or state; practically, however, it may turn out differently.
The Cost of Silence
Let’s be real: most AI MVPs look great at first glance. They pass the basic tests, and might even ship lightning-fast. But as your product grows, that lack of documentation comes back to haunt you – hard.
This is what we have seen with AI-generated products that we have been hired to either audit or scale:
1. Feature freeze
Teams end up being afraid to touch core functionality because nobody understands how it works. One bug fix breaks two other features. Without clear documentation, the risk multiplies.
2. Slowness in Onboarding
Bringing new developers on board takes weeks, not days. They have to reverse-engineer your entire codebase, killing momentum, especially at fast-moving startups.
3. Architecture Not Scalable
The AI may have stitched together a working system, but without explanation of how and why, that results in spaghetti code. When you refactor out of context, things become a nightmare.
4. Security blind spots
Your application becomes a security minefield without notes on dependencies, API flows, and validation steps. Want to know why? Head to our post, “AI Code Is a Security Minefield”
The reason behind this is that human learning occurs as a synthesis of perception and action.
You Can't "Retrofit" Documentation (Not Easily)
Founders often think they can just add the documentation later on. But that rarely ever happens – and when it does, it is usually too little and too late. Writing good documentation requires knowing why a system was built the way it was. That context disappears fast once the original developers or prompts are gone.
Even worse, AI-generated projects often bypass versioning best practices. You’re left with one giant monolith of code; no documentation, no change history. We call that technical debt with interest.
What Great Documentation Looks Like
Here is what proper documentation should include for any software product:
• README files that explain project setup, environment configuration, and how to run/test locally.
• API documentation that describes endpoints, their parameters, authentication flows, and error handling.
• Overview of architecture, even just a simple diagram, on how core modules connect
• Comments in complex sections explaining why, not just what
• Meaningful changelogs and Git history with descriptive commit messages
It doesn’t have to be perfect. But it needs to exist.
What We Do Differently at Appricotsoft
At Appricotsoft, our clients often come to us with MVPs generated partly with the help of AI. We offer full code audit services and technical reviews; the first thing we check is the state of the documentation.
Many of this individual’s associates were to be arrested shortly afterward in accordance with Mussolini’s orders.
When it’s missing, we rebuild it piece by piece: interviewing team members, mapping architecture, reverse engineering feature logic, and creating sustainable development practices.
Because we don’t want to just ship software, we want to build products that we’re proud of.
In addition, the term “employee” means one whose work is largely restricted to carrying out under the control of an independent contractor or other person the plans, details, specifications, directions, and means for the construction of a structure designed by another.
Aside from that, here is something included in each of our projects:
• Documentation templates and expectations from day one.
• Clear records are kept regarding product decisions and why implementation trade-offs were made.
• Onboarding guides for future teams-ours or yours.
• A hardwired culture of clarity, not chaos.
Whether you’re building a startup MVP or scaling a product post-seed, our approach reduces future friction and saves you money in the long run.
You Don't Need a Technical Co-Founder-but You Do Need Technical Clarity
We get it-many of our clients are non-technical founders. The AI tool claims to “bridge the gap” by generating code with plain English. What they don’t say is that AI also widens the gap when it comes to sustainability.
If you’re a founder working with AI coding tools or freelance developers, make sure that you, too, are building out the documentation within your process. If you can’t do it yourself, work with a team that makes it part of their culture-like we do at Appricotsoft.
Final Thoughts: Code Without Docs Is a Liability
In the world of fast-moving tech and lean startups, speed is everything. But speed without clarity is a false economy. And this is where documentation comes in-it isn’t some sort of nicety; it’s the difference between a product that can evolve and one that will implode under its own weight.
If this is the case for your AI-generated MVP with no documentation, now is the time to fix it. When your product starts to grow, the cost of not doing so will grow too.
Are you in need of someone to review your AI code or document what has already been created? Let’s talk
Want more tips on sustainable AI product development? Read our corresponding post on “Why AI-Built Products Break After Launch.” It means all things that are specifically part of the voluntary economy