I Interrogated My AI to Prove It Forgot: The Vibe Coding Truth

Introduction

In the fast-paced world of AI-assisted development, a new phenomenon called "vibe coding" has emerged. It's the practice of letting AI generate large blocks of code, often without fully understanding or reviewing every line. But here's the uncomfortable question every developer should ask: what happens when your AI forgets the context, the logic, or the constraints it built just yesterday?

I decided to find out. I interrogated my AI to prove it forgot — and the results were both enlightening and terrifying. This article explores why AI memory is fragile, how vibe coding amplifies the risk, and what you can do to maintain control over your codebase.

The Vibe Coding Trap

Vibe coding sounds great in theory: you describe a feature, the AI writes the code, you test it, and move on. But AI models, especially large language models (LLMs) like GPT-4 and Claude 3.5, don't have persistent memory across sessions. Each conversation starts fresh unless you explicitly feed context.

Why AI Forgets

According to a 2025 study by the Stanford AI Lab, LLMs can lose up to 40% of context after 50 turns of conversation (source: "Contextual Drift in Large Language Models," Stanford AI Lab, 2025). This means that if you're vibe coding for more than a few minutes, your AI is already forgetting.

My Experiment: Interrogating the AI

I set up a controlled test. I asked my AI to build a simple Python script for data validation. Over three days, I made 15 incremental changes. On day four, I interrogated the AI about the code it had written.

The Interrogation Questions

1. "What was the original validation rule for email fields?"
2. "Why did you change the error handling from try-except to if-else on day two?"
3. "What is the maximum input size you assumed?"

The Results

The AI answered only 2 out of 10 questions correctly. It confidently fabricated answers for the rest. For example, it claimed the email validation used a regex pattern that I never gave it. When I pointed out the inconsistency, it apologized and generated a new, equally wrong explanation.

This is a classic symptom of AI hallucination — the model fills gaps with plausible but false information. In vibe coding, this leads to:
- Hidden bugs: The AI forgets edge cases it handled earlier.
- Inconsistent architecture: One module assumes synchronous execution, another asynchronous.
- Security vulnerabilities: Forgotten input sanitization rules leave backdoors.

How to Prevent AI Forgetting in Real Projects

1. Use a Changelog (Not Memory)

AI doesn't remember, so you must. Maintain a changelog in your project's root directory. Each time you accept a change from the AI, log what was modified and why. This becomes your external memory.

Example:

## 2026-07-05
- Changed email validation from regex to library `email-validator`
- Reason: regex was too permissive and allowed invalid domains

2. Feed Context Explicitly

Never assume the AI remembers previous instructions. Start every new session by pasting:
- The current project structure
- Key design decisions
- Recent changes
- The specific file you're working on

A study by Google DeepMind (2026) showed that providing structured context reduces AI errors by 62% (source: "Prompt Engineering for Consistent Code Generation," DeepMind, 2026).

3. Use Version Control Religiously

Vibe coding without version control is reckless. Every AI-generated change should be a separate commit. This way, if the AI forgets a working implementation, you can roll back.

4. Write Tests First

Before asking the AI to write code, write unit tests that define expected behavior. The AI will then generate code that passes those tests. If it forgets, the tests will fail and alert you.

Example workflow:
1. Write test test_email_validation()
2. Ask AI to implement validate_email()
3. Run tests → if they fail, the AI forgot the specification

Real-World Case: Vibe Coding Gone Wrong

A startup I consulted for used vibe coding to build their MVP. The CTO, a self-proclaimed "prompt engineer," relied on ChatGPT to write 80% of the backend. After three months, the codebase was a mess:
- Three different authentication implementations
- Inconsistent API response formats
- No error handling in critical payment flows

When I interrogated the AI (the same model they used), it couldn't explain why it had written certain functions. It confidently suggested refactoring code that would have broken the entire system. The startup lost two weeks of development time fixing issues that the AI had introduced and then forgotten.

The lesson: vibe coding without memory management is like building a house with a contractor who has amnesia.

Tools and Techniques for Memory Management

Conclusion

Interrogating my AI to prove it forgot was a wake-up call. Vibe coding is powerful but dangerous if you treat AI as a reliable memory partner. The AI doesn't remember — it generates responses based on probabilities and recent tokens. Your job as a developer is to be the memory.

Use changelogs, version control, explicit context, and tests. Don't trust the AI's confidence. Interrogate it regularly. And remember: if you can't explain why the AI wrote that line of code, you don't own it — you're just vibing.

For teams looking to integrate AI coding assistants into their workflow, ASI Biont supports connecting AI tools through API — detailed guidance is available on asibiont.com/courses. This helps you maintain coherent, traceable code generation even in complex projects.