Antigravity Prompt Engineering Guide — Crafting Instructions That Maximize AI Agent Accuracy

Why Prompt Engineering Defines Your Antigravity Productivity

Antigravity is a powerful IDE where AI agents autonomously generate and edit code, but the quality of their output depends heavily on how you write your prompts. The same feature can be implemented with wildly different quality depending on whether your instructions are vague or well-structured.

Prompt engineering is the discipline of designing instructions that elicit optimal responses from AI. Antigravity's agents are powered by Gemini models, and with the right prompts, you can dramatically improve code generation accuracy, refactoring quality, and debugging efficiency.

This guide walks through practical prompt engineering techniques you can use daily in Antigravity, complete with concrete code examples.

Core Principles: Five Elements of an Effective Prompt

To get the best results from Antigravity's AI agents, keep these five elements in mind.

1. Context (Background Information)

Provide the information the AI needs to understand your current situation.

❌ Bad:
"Build a login feature"

✅ Good:
"In a Next.js 16 App Router + TypeScript project,
implement Google OAuth login using NextAuth.js v5.
Add a session provider to the existing src/app/layout.tsx
and create a new login page at src/app/login/page.tsx."

2. Specific Goal

Clearly define what you expect as output.

❌ Bad:
"Improve performance"

✅ Good:
"Reduce initial load time for the product listing page by
applying React.memo to memoize components and adding useMemo
for price calculations. The current ProductCard component
recalculates on every render, which is the bottleneck."

3. Constraints

State the rules and technical limitations explicitly.

✅ Example:
"Follow these constraints:
- No new external libraries (use existing dependencies only)
- Must run on Cloudflare Workers (Node.js fs module unavailable)
- Response time must be under 50ms per request"

4. Output Format

Specify how you want the results structured.

✅ Example:
"Output as a TypeScript type definition file (.d.ts).
Add JSDoc comments explaining the purpose of each type.
Use named exports exclusively."

5. Reference Examples

Provide samples of the expected output style.

✅ Example:
"Create a utility function following this pattern:

// Existing formatDate function (reference)
export function formatDate(date: Date, locale: string): string {
  return new Intl.DateTimeFormat(locale, {
    year: 'numeric',
    month: 'long',
    day: 'numeric',
  }).format(date);
}

Implement a formatCurrency function in the same style."

Structured Prompts for Precise Code Generation

When generating code in Antigravity, structured prompts significantly boost output quality. Here's a template you can adapt.

## Task
[Overview of the feature to implement]
 
## Tech Stack
- Framework: [Next.js 16 / React / Vue.js, etc.]
- Language: [TypeScript / Python, etc.]
- Other: [Libraries and tools in use]
 
## Requirements
1. [Specific requirement 1]
2. [Specific requirement 2]
3. [Specific requirement 3]
 
## File Structure
- src/components/[ComponentName].tsx — UI component
- src/hooks/use[HookName].ts — Custom hook
- src/lib/[utilName].ts — Utility function
 
## Constraints
- [Rules to follow]
 
## Reference
[Relevant existing code or expected behavior description]

Here's a real-world example using this template:

## Task
Implement incremental product search functionality
 
## Tech Stack
- Framework: Next.js 16 App Router
- Language: TypeScript
- Other: Tailwind CSS, Cloudflare D1 (database)
 
## Requirements
1. Fuzzy search across product names and descriptions
2. Apply 300ms debounce on input
3. Highlight matching text in search results
4. Show alternative suggestions when results are empty
 
## File Structure
- src/components/SearchBar.tsx — Search bar UI
- src/hooks/useProductSearch.ts — Search logic
- src/app/api/search/route.ts — API endpoint
 
## Constraints
- Must run on Cloudflare Workers
- Leverage D1's FTS5 for full-text search
- Response time under 100ms per request
 
## Reference
Follow the query patterns in existing src/app/api/products/route.ts

With this kind of structured prompt, Antigravity's agent generates the right files with proper dependencies and consistent coding style.

Prompt Techniques for Higher-Quality Refactoring

When refactoring, clarity about "what," "why," and "how" you want to change is essential.

The Before/After Pattern

Present the current state alongside the expected direction of change.

## Current Code (Issues)
- Multiple side effects mixed in a single useEffect
- Error handling limited to console.log inside try-catch
- Three instances of `any` type that need proper typing
 
## Expected Changes
1. Split useEffect by responsibility (data fetching / event listeners / cleanup)
2. Replace error handling with a unified pattern
   - Define custom error types
   - Set user-facing error messages
3. Replace `any` with proper types (let inference handle obvious cases)
 
## Do Not Change
- Component's external API (props types) must remain the same
- Do not modify test files (verify tests pass after refactoring)

Scope-Limited Instructions

For large-scale refactoring, avoid changing everything at once. Clearly limit the scope.

"Target only components in the src/components/Dashboard/ directory.
Apply the following refactoring:
1. Convert class components to function components with Hooks
2. Replace PropTypes with TypeScript interfaces
3. After each component change, verify existing .test.tsx files pass

Do not touch any files outside this directory."

Prompt Design for Maximum Debugging Efficiency

When asking the agent to investigate and fix bugs, providing structured context dramatically reduces debugging time.

## Bug Description
Clicking "Next" on the product listing page reloads
page 1 instead of showing page 2
 
## Reproduction Steps
1. Navigate to /products
2. 20 products display correctly
3. Click the "Next" button
4. URL changes to /products?page=2, but page 1 content persists
 
## Expected Behavior
Products 21–40 (page 2) should display
 
## Already Investigated
- API response: /api/products?page=2 returns correct data (verified in Network tab)
- useState page variable updates correctly (confirmed in React DevTools)
 
## Suspected Cause
The useEffect dependency array in src/hooks/useProducts.ts
may be missing the `page` variable

By organizing "symptoms," "reproduction steps," "expected behavior," and "investigated information," the agent can skip unnecessary exploration and zero in on the root cause.

Project-Level Prompt Design with AGENTS.md

Antigravity supports AGENTS.md files that define project-wide prompts. This eliminates the need to repeat the same constraints in every chat message.

# AGENTS.md
 
## Coding Standards
- Use TypeScript strict mode
- Standardize on arrow functions
- Use named exports for components
- Import order: React → external libs → internal modules → type definitions
 
## Error Handling
- Unify API errors through AppError class
- Manage user-facing messages via i18n keys
 
## Testing
- Add unit tests for all new code
- Place test files in the same directory as .test.ts(x)
 
## Deployment Environment
- Runtime: Cloudflare Workers (subset of Node.js APIs available)
- Database: Cloudflare D1 (SQLite-compatible)
- KV Store: Cloudflare KV

With common rules in AGENTS.md, your individual prompts can focus entirely on task-specific instructions. For a deeper dive into multi-agent configuration, check out the AGENTS.md Multi-Agent Architecture Guide.

Advanced Technique: Incremental Prompt Strategy

Attempting to implement complex features in a single prompt often overwhelms the agent and degrades quality. Breaking prompts into stages works much better.

Step 1: Design Review

"I want to implement an authentication system with the following requirements.
Don't write any code yet.
Please propose an implementation plan and file structure.

Requirements: NextAuth.js v5 + Google OAuth + Cloudflare KV session management"

Step 2: Start with Type Definitions

"Based on your proposed design, create the type definition files first.
- src/types/auth.ts — Authentication-related types
- src/types/session.ts — Session-related types
No implementation needed yet."

Step 3: Core Implementation

"Using the type definitions, implement the core authentication logic.
- src/lib/auth.ts — NextAuth configuration
- src/lib/session.ts — KV session management
Create tests alongside the implementation."

Step 4: UI Integration

"Integrate the authentication logic into the UI.
- src/components/LoginButton.tsx
- src/app/login/page.tsx
- src/middleware.ts (authentication guard)"

This incremental approach allows you to review and correct at each stage, significantly improving the final result.

Looking back

Prompt engineering is a critical skill for unlocking the full potential of Antigravity's AI agents. By incorporating the five elements — context, goal, constraints, output format, and reference examples — into structured prompts, you can dramatically improve code generation accuracy.

For more advanced agent workflow techniques, the Antigravity Practical Techniques Collection (Part 2) covers expert-level strategies.