10 Antigravity Mistakes That Kill Your Productivity (And How to Fix Them)
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Nick
December 27, 2025
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10 Antigravity Mistakes That Kill Your Productivity (And How to Fix Them)
Why Most Developers Use Antigravity Wrong
Google Antigravity is incredibly powerful—but power without knowledge leads to frustration. After analyzing thousands of developer interactions with AI coding tools and surveying the Antigravity community, we've identified the 10 most common mistakes that kill productivity.
According to the 2025 Stack Overflow Developer Survey, 87% of developers are concerned about AI code accuracy, and 81% worry about security. These concerns are valid—but most issues stem from user mistakes, not AI limitations.
Here are the mistakes you're probably making and exactly how to fix them.
Mistake #1: Vague, Ambiguous Prompts
The Problem
Most developers prompt like this:
"Add user authentication"
This gives Antigravity almost nothing to work with. Which authentication method? What fields? What UI? What happens after login?
The AI fills in the gaps with assumptions—assumptions that probably don't match what you want.
The Fix: Be Painfully Specific
"Implement email/password authentication with Supabase:
Sign up form with email, password (min 8 chars with number), and password confirmation
Sign in form with email and password
Password reset flow via email
After successful auth, redirect to /dashboard
Store user profile in profiles table with username and avatar_url
Use React Hook Form for validation
Show inline error messages for each field
Include loading states on submit buttons
Style with Tailwind CSS matching our existing design
Create all necessary components, server actions, and database schema."
Yes, it's more typing upfront. But it saves hours of back-and-forth corrections.
The Rule
Spend 2 minutes writing a detailed prompt to save 20 minutes fixing bad output.
This wastes tokens, creates inconsistency, and leads to code that doesn't match your project.
The Fix: Create a Comprehensive Rules File
# Project: E-commerce Platform
# Stack: Next.js 15, TypeScript, Supabase, Tailwind
## TypeScript
- Use strict mode with no any types
- Prefer interfaces over type aliases for objects
- Use type inference where obvious
- Export types from a central types/ directory
## React/Next.js
- Server Components by default
- Client Components only for interactivity
- Use Server Actions for mutations
- Implement proper loading.tsx and error.tsx
## Code Style
- Functions: arrow functions for components, regular for utilities
- Naming: camelCase for variables, PascalCase for components
- Max line length: 80 characters
- Use early returns to reduce nesting
## Error Handling
- Always handle errors explicitly
- Use typed error responses
- Log errors with context (user ID, action, timestamp)
- Show user-friendly messages, log technical details
## What NOT to do
- Never use console.log (use proper logger)
- Never commit secrets or API keys
- Never skip error handling
- Never use any type
Now every interaction with Antigravity automatically follows these rules.
Mistake #3: Accepting All Suggestions Without Review
The Problem
You're excited. The AI generates 500 lines of code in seconds. You accept all changes, run the app, and… it's broken. Or worse, it works but has subtle security flaws you don't notice until production.
The Fix: Review Like Your Job Depends On It (It Does)
Use this review checklist for every AI-generated code:
AI generates, you validate. Never deploy unreviewed AI code.
Mistake #4: Using Chat When You Should Use Agent Mode
The Problem
You're building a feature that spans 10 files. You use regular chat, prompting file by file. By the time you finish, the AI has forgotten the context from earlier files. Things don't connect properly.
The Fix: Use Agent Mode for Multi-File Tasks
Press Cmd+Shift+A and describe the entire feature at once:
"Create a complete blog system with:
Blog post model (title, slug, content, excerpt, featured image, status, author)
Admin CRUD pages for managing posts
Public listing and detail pages
Markdown rendering with syntax highlighting
SEO metadata generation
Related posts sidebar
Generate all models, migrations, components, and pages needed."
Agent Mode maintains context across all files and ensures they work together.
When to Use Each Mode
Task
Mode
Quick fix in one file
Chat (Cmd+L)
Generate a single component
Chat
Understand existing code
Chat
Multi-file feature
Agent Mode
Refactoring across codebase
Agent Mode
New project setup
Agent Mode
Mistake #5: Not Providing Context About Your Codebase
The Problem
You ask Antigravity to add a feature, but it doesn't know:
You have a custom auth system
You use a specific database wrapper
Your components follow a particular pattern
You have existing utilities it could reuse
Result: The AI creates code that doesn't integrate with your existing patterns.
The Fix: Give Context Upfront
"Context about our codebase:
We use a custom useAuth hook (located at src/hooks/useAuth.ts) for authentication
Database calls go through our supabaseClient wrapper (src/lib/supabase.ts)
We have a Button component with loading states at src/components/ui/Button.tsx
API responses use the format { data, error, status }
Now, create a user profile page that integrates with these existing patterns."
Or use the @ symbol to reference files:
"Create a user profile page. Reference @useAuth for authentication, @supabaseClient for database, and @Button for the save button styling."
The Rule
The more context you provide, the better the output matches your codebase.
Mistake #6: Ignoring Antigravity's Questions
The Problem
Antigravity asks: "Should the user be redirected to /dashboard or /profile after login?"
You ignore it and say: "Just do it."
The AI guesses. It guesses wrong. You have to redo the work.
The Fix: Answer Every Question
When Antigravity asks for clarification, it's trying to give you exactly what you want. Take 10 seconds to answer:
"Redirect to /dashboard after login. Also, store the original destination URL so we can redirect there if the user was trying to access a protected page."
Mistake #7: Trying to Fix Bad Output Instead of Regenerating
The Problem
The AI generates code that's 70% what you want but fundamentally wrong in structure. Instead of starting fresh with a better prompt, you spend an hour trying to fix the broken output.
The Fix: Know When to Regenerate
Regenerate if:
The overall approach is wrong
The structure doesn't fit your needs
It would take longer to fix than to start over
The AI misunderstood your core requirement
Iterate if:
The approach is right but details are off
You need to add a missing feature
There's a specific bug to fix
The changes are small and localized
When regenerating, improve your prompt based on what went wrong.
Mistake #8: Not Leveraging MCP Servers
The Problem
You're manually copying database schemas, API documentation, and file contents into your prompts. This wastes time and often results in outdated or incomplete information.
Now Antigravity can query your actual database schema and read your GitHub issues.
Mistake #9: Asking for Too Much at Once
The Problem
Your prompt:
"Build me a complete e-commerce platform with products, cart, checkout, payments, user accounts, admin dashboard, inventory management, shipping integration, analytics, and email notifications."
Result: Superficial implementation of everything, production-ready implementation of nothing.
The Fix: Build in Focused Phases
Phase 1: Foundation
"Set up Next.js project with authentication, database schema for users and products, and basic layout."
"Implement shopping cart with add/remove items, quantity updates, and persistent cart for logged-in users."
Phase 4: Checkout
"Build checkout flow with address collection, shipping options, and Stripe payment integration."
Each phase gets full attention. Each phase is testable. Each phase is production-ready before moving on.
The Rule
One focused task at a time. Complete it before starting the next.
Mistake #10: Not Learning From the Generated Code
The Problem
You use Antigravity as a black box. Code goes in, code comes out. You never understand what it generated or why.
Result: You can't debug issues, you can't extend the code, and you don't grow as a developer.
The Fix: Use Antigravity as a Teacher
After generating code, ask:
"Explain how this authentication flow works step by step. Why did you choose this approach over alternatives?"
"What are the security considerations in this code? Are there any edge cases I should be aware of?"
"How would I extend this to add [feature]? What would I need to change?"
Treat every interaction as a learning opportunity.
Productivity Scorecard
Rate yourself on each mistake:
Mistake
Impact
You?
Vague prompts
High
☐
No .antigravity file
High
☐
Not reviewing code
Critical
☐
Wrong mode (chat vs agent)
Medium
☐
Missing context
Medium
☐
Ignoring questions
Medium
☐
Fixing instead of regenerating
Medium
☐
Not using MCP servers
Low
☐
Too much at once
High
☐
Not learning from output
Medium
☐
0-2 mistakes: You're an Antigravity power user.
3-5 mistakes: Room for improvement. Focus on the high-impact fixes.
6+ mistakes: You're leaving massive productivity on the table.
FAQs
How long should I spend on a prompt?
For simple tasks: 30 seconds. For complex features: 2-5 minutes. The investment pays off in output quality.
Should I always use Agent Mode for big features?
For anything touching more than 3 files, yes. Agent Mode maintains context that regular chat loses.
How do I know when the AI is wrong?
Review the logic, test thoroughly, and if something seems off, ask the AI to explain its reasoning.
What if the AI keeps making the same mistake?
Update your .antigravity rules to explicitly forbid the pattern.
Conclusion
Google Antigravity is a productivity multiplier—if you use it correctly. These 10 mistakes are the difference between developers who struggle with AI coding and those who ship 10x faster.
Start with the high-impact fixes: clear prompts, .antigravity rules, and code review. Then optimize the rest. Within a week, you'll wonder how you ever coded without AI.
Next Steps
Create a .antigravity file for your current project
Install 2-3 relevant MCP servers from our directory