When people talk about the risks of Vibe Coding, they usually mention poor code quality, security holes, and future maintenance pain. Those problems are real, but for a solo developer working on a long-running project, something else is often more dangerous: requirement drift.
Requirement drift is not a single bug or an obviously bad commit. It is more like the project slowly losing its sense of direction. Features keep growing, the code still runs, and the docs may still exist, but you can no longer clearly explain why certain modules were split that way, why a constraint was kept, or where the next change should attach.
The problem tends to surface when you try to add a seemingly simple feature. You change one place and another breaks; you fix that one and something else bends out of shape. After several rounds of patch-style repair with AI, you realize the missing piece is not code. It is your complete map of the project.
Why Long Projects Drift So Easily
The appeal of Vibe Coding comes from speed. You describe a vague need, and AI quickly generates a screen of code. You add one more sentence, and it keeps going. This is efficient for short tasks, but in long projects it creates two compounding problems.
First, the model forgets. This does not always mean it can no longer see the context at all. More often, the important information inside a long context loses weight. Early product goals, architecture choices, naming conventions, and boundary conditions are gradually buried under debugging details, temporary fixes, and intermediate states. The noisier the context window becomes, the easier it is for the model to grab the wrong priority.
Second, humans forget too. AI lowers the friction of writing code so much that it skips the process that used to force you to think clearly. Before, unclear requirements would stop you from writing. Now, unclear requirements can still produce a first version. Vague intent is quickly solidified into code, and explaining it later becomes harder and harder.
This is why solo projects hit this wall so easily. You are the product manager, developer, tester, and operator at the same time. Coding pushes you to move fast; product judgment requires you to slow down and ask what you actually want. AI accelerates the former, but it cannot do the latter for you.
The Real Problem Is Lost Intent
Code can describe what a system does, but it is bad at explaining why it does it that way.
Projects often fall apart not because one piece of code is especially poor, but because the intent behind decisions is scattered across long conversations, temporary prompts, personal memory, and fragmented commits. When you reopen the project, you can see the result, but not the reasoning.
AI cannot reliably solve this for you. It can read code, trace call chains, and add tests, but it cannot reconstruct the original tradeoffs with confidence. It may even rationalize the wrong direction based on the local code it sees, pushing the drift deeper.
So the key question in Vibe Coding is not how to make AI never forget. It is how to avoid keeping the project’s memory only inside a conversation.
Move Project Memory Into Files
The best way to fight requirement drift is to put intent, constraints, and state into the repository, so every new session can read them again.
The lightest step is to write a CLAUDE.md, AGENTS.md, or a similar rules file. It does not need to be long, but it should clearly state:
- What problem the project solves.
- What the current product goal is.
- Which architecture boundaries must not be changed casually.
- The common naming, directory, testing, and release rules.
- Which practices are explicitly forbidden.
The value of this file is not that it is a more advanced prompt. Its value is that it pins the most easily drifting constraints near the start of every session. It should be short, stable, and readable, without being stuffed with temporary details.
The next step is specification-driven work. Do not ask AI to “build a feature” right away. First, turn the need into a checkable specification:
- What is the user scenario?
- What are the inputs and outputs?
- What counts as success?
- What is explicitly out of scope?
- Which existing modules are affected?
- Which tests or verification steps are needed?
A specification makes AI work around clear intent, not around a casual prompt. This is especially important for solo projects, because there is no product manager forcing vague ideas into clear requirements for you.
Handoff Before Ending Each Session
Long projects should not be forced through one endless conversation. The longer the session, the more debugging noise accumulates, and the easier it is for judgment quality to decline.
A steadier habit is to write the session state into a file before stopping, such as PROGRESS.md, TODO.md, or a task log:
- What was completed in this session.
- Which important files changed.
- Which issues remain unresolved.
- Where the next session should start.
- Which constraints must not be broken.
Then commit the code. The next time you open a new session, AI does not need to infer the project state from a long chat history. It can read the handoff material from the repository.
This looks plain, but it works. It moves memory out of a drifting conversation and into a versioned file system.
When To Stop And Reorganize
If you are already seeing these signs, pause new feature generation and clean up the project first:
- The same issue is fixed repeatedly and keeps reappearing elsewhere.
- AI frequently changes modules unrelated to the task.
- You cannot explain why a directory, interface, or state field exists.
- New features increasingly depend on patches instead of clear design.
- Documentation, code, and actual behavior stop matching each other.
- It takes a long time to regain the project state.
At that point, continuing to Vibe Code will only stack the confusion higher. A better order is to rebuild the project map first, then write code: document modules, add specifications, remove abandoned paths, add tests, and record important decisions as ADRs or short design notes.
What Solo Projects Really Need To Protect
AI can help you write code, inspect bugs, add tests, and refactor local implementations. It cannot hold the product intent for you over the long term.
For a solo project, the thing to protect is not “making AI write a little more every time.” It is knowing why the product exists, why the next step matters, and which boundaries must not move. As long as that remains clear, AI is an accelerator. Once that drifts, AI only expands the deviation faster.
So the most important Vibe Coding habit is not chasing the perfect prompt. It is regularly writing requirements, progress, constraints, and decisions back into the repository. Conversations can move fast. Project memory has to stay stable.
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