AI Coding Safety Checklist for New Developers (Print-Friendly)

AI coding assistants can save time and help you learn—but they also make it easy to ship insecure, outdated, or simply wrong code if you trust them too much. This checklist is designed for new developers who want to move fast without turning their projects into a security or maintenance headache. If you haven’t picked your first AI coding assistant yet, read our Best AI Coding Assistants for New Developers comparison. You can also use the Print / Save as PDF button at the end of this page to keep a copy next to your editor

Use it next to your IDE and run through it whenever you accept AI-generated code or ship a feature that relied heavily on an assistant.

How to Use This Checklist

Treat AI-generated code like code from a stranger on the internet: assume it is unsafe until you review and test it. Before you merge, skim this list and make sure you can honestly answer “yes” to each relevant item for your change.

You can also adapt this into a team standard: add the most important points to your pull request template or code review checklist.

Before You Prompt the AI

• Have you defined the task clearly in your own words (what you want, inputs, outputs, edge cases)?
• Are you avoiding any secrets, customer data, or proprietary information in the prompt?
• Do you know roughly how you’d solve the problem without AI, at least at a pseudocode level?
• For security-sensitive areas (auth, payments, data protection), have you considered whether this should be written manually or reviewed by someone experienced?

If the answer to any of these is “no”, refine your understanding or scope before you ask the assistant to generate code.

When You Receive AI-Generated Code

• Can you explain, in your own words, what each major block of code is doing?
• Did you check for hardcoded secrets, API keys, test passwords, or debug credentials in the generated code?
• Did you confirm that every imported library, function, or API actually exists and is current in the official documentation?
• Are there obvious missing checks: input validation, error handling, or boundary checks around user data and external calls?

If you hit unknown functions or suspicious libraries, pause and verify them manually or replace them with well-known alternatives.

Security-Specific Checks

• Are you avoiding AI-generated implementations for authentication, authorization, encryption, and password handling unless a senior developer reviews them?
• Does any database access use parameterized queries or an ORM, rather than string-concatenated SQL?
• Are outputs correctly encoded for their context (HTML, JSON, SQL, command line) to reduce injection risks?
• Have you checked AI-suggested dependencies for last update date, known vulnerabilities, and suspicious package names?

When in doubt, prefer framework defaults and well-documented patterns over clever but opaque AI-invented solutions.

Testing and Review

• Have you written or updated unit tests that cover happy paths, edge cases, and obvious failure conditions for AI-generated code?
• Did you run your test suite locally (or in CI) and confirm everything passes before merging?
• Has at least one human reviewer looked at the changes, focusing especially on AI-generated sections and security-sensitive code paths?
• If the change touches authentication, payment, or data access, did you test it in a staging environment with realistic (but anonymized) data?

If tests are missing or failing, treat that as a blocker, not a “later” task.

Data, Access, and Privacy

• Do you understand how your chosen AI assistant stores prompts and code (logging, retention, training use)?
• Are you using local or self-hosted options (like local models or privacy-focused tools) for sensitive or regulated projects?
• Is access to AI integrations (e.g., connected repos or cloud projects) limited to the minimum set of people and services that actually need it?
• Have you avoided connecting AI tools directly to production credentials or live data sources?

If you’re unsure about the data policy of a tool, escalate or choose a more conservative configuration before using it on critical code.

Habits to Avoid “Vibe Coding”

• Did you start from a small, well-defined task instead of asking the AI to build a whole feature or service at once?
• Are you reviewing AI changes in small diffs and limiting multi-file edits until you’re comfortable understanding the impact?
• Do you regularly spend time coding without AI assistance (for example, on small exercises) to keep your own skills sharp?
• Do you periodically re-read your own code (not the AI’s explanation) to build intuition for patterns and trade-offs?

These habits make AI a force multiplier for skills you actually have, instead of a crutch that hides gaps in understanding.

For a broader view of how to learn with AI safely, see AI Tools for New Developers: A Beginner’s Safety Guide.

One-Page Printable Checklist (Summary Items)

You can condense this article into a single page of yes/no questions to keep beside your editor:

• Did I avoid putting secrets or real customer data into prompts?
• Do I understand what this AI-generated code does, line by line?
• Did I verify all external APIs, libraries, and dependencies in official docs?
• Does this code validate inputs and handle errors safely?
• Did I run tests (including new ones for this code) and see them pass?
• Has a human reviewed the changes, especially around auth, payments, or data access?
• Am I using a configuration or tool that respects my project’s privacy and security requirements?

You can format these into a PDF or checklist graphic and link it from your main AI safety guide and tools comparison to use as a simple lead magnet.

Disclosure

This checklist mentions several AI coding tools that we recommend through affiliate partnerships. Thank you for supporting our work!