When to trust AI-generated code

Greg Foster
Greg Foster
Graphite software engineer
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Table of contents

AI-powered coding tools like GitHub Copilot, Amazon CodeWhisperer, and ChatGPT have become part of daily development workflows. They autocomplete functions, generate snippets, and support rapid iteration. By mid-2023, GitHub Copilot had already contributed over 3 billion accepted lines of code.

Given this adoption, the big question is: When can you trust AI-generated code? This article explores where it's reliable, when it needs scrutiny, how vibe coding fits in, and how review tools like Diamond can help.

AI coding assistants work well for:

  • Boilerplate code – setters/getters, REST endpoints, config files
  • Common logic – using standard libraries or solving known problems
  • Rapid prototyping – quickly building MVPs or proofs of concept

A 2023 study found GPT-3.5 generated correct Java functions ~90% of the time. Many tools also build in safety features:

  • CodeWhisperer scans for security issues and flags licensing risks
  • Copilot adjusts to your project's context to generate better suggestions

These tools are best used when the logic is simple, the solution is known, and outputs are easy to verify through tests or your own tooling.

AI isn't always correct, especially for complex or high-stakes scenarios. Be cautious when:

  • Security is involved – AI may introduce SQL injection, hardcoded secrets, or unsafe access patterns. Run scans, use linters, and verify every line.
  • Logic matters – AI can hallucinate invalid logic or use incorrect variables. Review closely when implementing business rules or algorithms.
  • Performance is critical – Generated code may not handle edge cases or scale well. Always benchmark and profile before shipping.
  • Licensing is unclear – Sometimes the AI echoes open-source code. If you're unsure about its origin, rewrite it yourself.

Remember, treat AI code like a junior developer's contribution: often helpful, but it needs a review.

Vibe coding is an emerging style of fast, AI-driven development. You describe what you want, accept AI changes with minimal edits, and quickly iterate. It's ideal for:

  • Side projects and internal tools
  • Hackathons and demos
  • First-draft experiments

Vibe coding lets developers (and non-developers) build fast, skipping boilerplate and focusing on intent. It's similar to low-code development in its speed and accessibility.

This speed-first mindset becomes dangerous when used in production:

  • Code may be poorly reviewed and fragile
  • Bugs or security flaws can sneak in
  • It becomes hard to maintain or debug later
  • You're still accountable for what ships, regardless of who wrote it

Vibe coding works well in early development. But for anything going live, bring in manual review, testing, and a healthy dose of skepticism.

To catch problems missed during vibe coding or rapid development, tools like Diamond by Graphite are key. Diamond acts as an AI reviewer that:

  • Analyzes pull requests in your repo
  • Reviews code with full project context, not just diffs
  • Flags bugs, edge cases, performance issues, and inconsistencies
  • Suggests natural-language fixes developers can easily apply

Diamond has caught logic bugs, missing null checks, and risky regex patterns in production-ready code. It helps developers move fast while still guarding quality. Some teams say it even catches issues before CI runs.

Because Diamond can enforce your team's standards, it offers consistency across human- and AI-written code. For teams embracing AI tools, it provides the guardrails to ship safely.

AI-generated code is a powerful tool—especially for speeding up development and reducing grunt work. But speed shouldn't replace caution. Use AI for well-known tasks and fast iterations, but stay alert when quality and correctness matter.

Whether you're prototyping with vibe coding or integrating AI into your dev process, don't skip validation. Pair generation with review. Tools like Diamond let you scale safely: they bridge the gap between experimentation and production readiness.

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