Sora AI Video Tips: Best Practices for Prompts, Efficiency & Google Antigravity

Introduction

The landscape of software development has undergone a seismic shift with the integration of AI coding assistants. Tools like GitHub Copilot, ChatGPT, and Claude have evolved from novelty to necessity, fundamentally altering how we write, review, and maintain code. However, many developers—even those who regularly use these tools—are barely scratching the surface of their potential.

This article is designed for developers who have already integrated AI assistants into their workflow but are ready to move beyond basic code completion. You know how to ask for a function, but do you know how to optimize your prompts for maximum efficiency? Can you leverage AI for legacy code refactoring? Are you generating comprehensive test suites in seconds?

Over the next few pages, I'll share 7 specific, actionable strategies with real prompts, code snippets, before/after comparisons, and measurable productivity gains. By the end, you'll have a toolkit of techniques that can reduce your development time by 50-90% on common tasks.

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Tip 1: Use Context-Rich Prompts for Code Generation

Why This Improves Workflow

The single biggest mistake developers make with AI coding assistants is providing vague, underspecified prompts. This forces the AI to make assumptions, which inevitably leads to outputs that require multiple rounds of correction. Context-rich prompts eliminate this ambiguity by providing the AI with your complete problem scope upfront.

The Psychology Behind It

AI models work best when they have clear constraints and requirements. By specifying data types, edge cases, error handling, and stylistic preferences in your initial prompt, you're essentially providing the AI with a mini-specification document. This dramatically reduces the iteration cycle from "generate, test, correct, regenerate" to "generate, verify, done."

Real Prompt Example

Poor Prompt:

Write a Python function to sort a list.

Optimized Prompt:

Write a Python function that sorts a list of dictionaries by a 'priority' key in descending order, handling missing keys gracefully. Use type hints and include a docstring.

Code Snippet

Before (generic output):

def sort_list(lst):
    return sorted(lst)

After (context-rich output):

from typing import List, Dict, Any

def sort_by_priority(data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Sort list of dicts by 'priority' key descending; missing keys treated as lowest priority."""
    return sorted(data, key=lambda x: x.get('priority', float('-inf')), reverse=True)

Productivity Comparison

• Before: 3–4 iterations to refine output (approximately 5-8 minutes)
• After: 1–2 iterations (approximately 2-3 minutes)
• Time saved: 50-60% on initial generation

Pro Tip

Include a "negative example" in your prompt: "Don't modify the original list; return a new sorted list." This prevents the AI from generating code with side effects you didn't anticipate.

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Tip 2: Leverage AI for Refactoring Legacy Code

Why This Improves Workflow

Every developer inherits legacy code. Whether it's callback hell in JavaScript, deeply nested conditionals, or monolithic functions that violate the Single Responsibility Principle, refactoring is a necessary but tedious task. AI assistants excel at pattern recognition and can transform messy code into clean, modern implementations with minimal input.

The Hidden Benefit

Beyond the obvious time savings, AI refactoring ensures consistency. When you manually refactor a 500-line function, you're likely to introduce subtle bugs. AI models, when given clear instructions, maintain functional equivalence while improving structure.

Real Prompt Example

Refactor this JavaScript code to use async/await instead of nested callbacks. Maintain the same functionality and error handling. Here's the code: [paste legacy code]

Code Snippet

Before (callback hell):

function fetchData(callback) {
    getUserId(function(err, id) {
        if (err) return callback(err);
        getUserData(id, function(err, data) {
            if (err) return callback(err);
            callback(null, data);
        });
    });
}

After (async/await):

async function fetchData() {
    try {
        const id = await getUserId();
        const data = await getUserData(id);
        return data;
    } catch (err) {
        throw err;
    }
}

Productivity Comparison

• Before: Manual refactoring took ~30 minutes for a 100-line function (including testing)
• After: AI refactoring in 2 minutes (plus 5 minutes for verification)
• Time saved: 93% reduction

Advanced Technique

For large refactoring projects, break the task into phases. First, ask the AI to "identify all functions that could benefit from async/await." Then, refactor one function at a time. This incremental approach reduces the risk of introducing systemic errors.

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Tip 3: Generate Unit Tests with Edge Cases

Why This Improves Workflow

Unit testing is arguably the most important yet most neglected aspect of software development. When deadlines loom, tests are often the first thing to be cut. AI assistants can generate comprehensive test suites in seconds, covering edge cases you might never have considered.

The Edge Case Advantage

Human developers naturally focus on "happy path" scenarios. AI models, trained on millions of codebases, have seen every possible edge case—empty inputs, type mismatches, boundary values, and unexpected data structures. By explicitly requesting edge case coverage, you get tests that would take hours to write manually.

Real Prompt Example

Generate pytest unit tests for this function. Include tests for: normal input, empty list, single element, duplicate values, and invalid type. Use parametrize for efficiency. Function: [paste code]

Code Snippet

Before (manual test):

def test_sort():
    assert sort_by_priority([{'priority': 2}, {'priority': 1}]) == [{'priority': 2}, {'priority': 1}]

After (AI-generated with edge cases):

import pytest

@pytest.mark.parametrize("input_list, expected", [
    ([{'priority': 2}, {'priority': 1}], [{'priority': 2}, {'priority': 1}]),
    ([], []),
    ([{'priority': 1}], [{'priority': 1}]),
    ([{'priority': 2}, {'priority': 2}], [{'priority': 2}, {'priority': 2}]),
    ([{'other': 3}], [{'other': 3}]),  # missing key
])
def test_sort_by_priority(input_list, expected):
    assert sort_by_priority(input_list) == expected

def test_invalid_type():
    with pytest.raises(TypeError):
        sort_by_priority("not a list")

Productivity Comparison

• Before: Writing 5 tests manually took 15 minutes (including edge case brainstorming)
• After: AI generates 6+ tests in 30 seconds (plus 2 minutes for review)
• Time saved: 97% reduction

Pro Tip

Ask the AI to "generate tests that would catch regressions from future code changes." This produces tests that test invariants rather than specific implementations, making them more robust over time.

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Tip 4: Use AI for Code Review and Bug Detection

Why This Improves Workflow

Code review is essential but time-consuming. AI assistants can act as an automated second pair of eyes, catching common mistakes, security vulnerabilities, and performance issues before they reach human reviewers. This shifts the human review process from "finding bugs" to "evaluating design decisions."

The Security Angle

Many developers don't realize that AI models are particularly good at identifying security antipatterns—SQL injection vulnerabilities, improper input validation, hardcoded credentials, and unsafe deserialization. By explicitly asking for a security audit, you can catch issues that might otherwise slip through.

Real Prompt Example

Review this Python code for: 1) potential bugs, 2) security vulnerabilities, 3) performance bottlenecks, 4) readability improvements. Provide specific line numbers and suggested fixes. Code: [paste code]

Code Snippet (Before Review)

def process_user_input(user_data):
    query = f"SELECT * FROM users WHERE id = {user_data['id']}"
    result = db.execute(query)
    return result.fetchall()

AI Review Output

Issues found:
1. **SQL Injection (Line 2)**: Using f-string interpolation for SQL queries is dangerous. Use parameterized queries.
2. **Missing Input Validation (Line 1)**: No validation that user_data contains 'id' key. Could cause KeyError.
3. **Performance (Line 3)**: fetchall() returns all rows. Consider using fetchone() if only one result expected.
4. **Readability (Line 2)**: Consider using a query builder or ORM for better maintainability.

Suggested fix:
def process_user_input(user_data):
    if 'id' not in user_data:
        raise ValueError("Missing 'id' in user data")
    query = "SELECT * FROM users WHERE id = %s"
    result = db.execute(query, (user_data['id'],))
    return result.fetchone()

Productivity Comparison

• Before: Manual review of 100 lines took 15-20 minutes
• After: AI review in 30 seconds (plus 5 minutes for human verification)
• Time saved: 75-80% reduction in review time

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Tip 5: Generate Documentation and Comments Automatically

Why This Improves Workflow

Documentation is the first thing developers abandon when deadlines approach. AI assistants can generate comprehensive docstrings, inline comments, and even README files from your code. This ensures that your codebase remains maintainable without adding to your workload.

The Consistency Factor

AI-generated documentation follows consistent patterns. When you manually write comments, you might be verbose in some places and sparse in others. AI models produce uniform documentation that makes your codebase look professionally maintained.

Real Prompt Example

Generate Google-style docstrings for all functions in this module. Include: description, Args, Returns, Raises, and Examples sections where applicable. Module: [paste code]

Code Snippet

Before (no documentation):

def calculate_discount(price, discount_percent, is_premium=False):
    if is_premium:
        discount_percent += 5
    final_price = price * (1 - discount_percent / 100)
    return max(0, final_price)

After (AI-generated docstrings):

def calculate_discount(price: float, discount_percent: float, is_premium: bool = False) -> float:
    """
    Calculate the final price after applying a discount.
    
    Args:
        price (float): Original price of the item (must be non-negative).
        discount_percent (float): Discount percentage (0-100).
        is_premium (bool, optional): Whether the customer has premium status.
            Premium customers receive an additional 5% discount. Defaults to False.
    
    Returns:
        float: Final price after discount (always >= 0).
    
    Raises:
        ValueError: If price is negative or discount_percent is outside 0-100.
    
    Examples:
        >>> calculate_discount(100, 10)
        90.0
        >>> calculate_discount(100, 10, True)
        85.0
        >>> calculate_discount(0, 50)
        0.0
    """
    if price < 0:
        raise ValueError("Price must be non-negative")
    if not 0 <= discount_percent <= 100:
        raise ValueError("Discount must be between 0 and 100")
    
    if is_premium:
        discount_percent += 5
    final_price = price * (1 - discount_percent / 100)
    return max(0, final_price)

Productivity Comparison

• Before: Writing documentation for 5 functions took 20 minutes
• After: AI generates documentation in 1 minute
• Time saved: 95% reduction

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Tip 6: Optimize Prompts for Multi-Step Workflows

Why This Improves Workflow

Complex development tasks often require multiple steps: generate code, test it, optimize it, document it. Instead of treating each step as a separate conversation, you can chain prompts together to create a seamless workflow.

The Chain Technique

Start with a high-level prompt, then refine with subsequent prompts that build on previous outputs. This creates a context-rich environment where the AI understands your overall goal, not just the current step.

Real Prompt Example

Step 1 (Generate):

Write a Python class for a rate limiter that allows N requests per minute per user. Use a sliding window algorithm.

Step 2 (Test):

Generate pytest tests for the rate limiter class from the previous response. Include tests for: under limit, exactly at limit, over limit, multiple users, and time-based window reset.

Step 3 (Optimize):

Optimize the rate limiter for high concurrency. Replace the list-based sliding window with a deque and add thread safety with a lock.

Step 4 (Document):

Write a README section explaining how to use this rate limiter, including a code example and performance characteristics.

Productivity Comparison

• Before: Manual multi-step development took 2-3 hours
• After: AI chained workflow in 20-30 minutes
• Time saved: 80-85% reduction

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Tip 7: Customize AI Behavior with System Prompts

Why This Improves Workflow

Most developers use default AI settings, but advanced users can customize behavior with system prompts. These are initial instructions that set the context for the entire conversation, influencing the AI's tone, depth, and focus.

The Power of Persona

By assigning the AI a specific persona—"You are a senior Python developer at Google with expertise in performance optimization"—you get outputs that match that persona's knowledge level and communication style.

Real System Prompt Example

You are a lead developer with 15 years of experience in Python and system design. When responding:
1. Always include performance implications of your suggestions.
2. Prefer standard library solutions over third-party packages.
3. Include error handling and edge cases.
4. Use type hints and comprehensive docstrings.
5. Explain your reasoning briefly.

Now, help me design a caching layer for a Django REST API.

Resulting Output Quality

With this system prompt, the AI will:

• Suggest functools.lru_cache before reaching for Redis
• Discuss cache invalidation strategies
• Include thread-safety considerations
• Provide production-ready code with proper error handling

Productivity Comparison

• Before: 3-4 iterations to get production-quality code
• After: 1-2 iterations with the right system prompt
• Time saved: 50-60% reduction in iteration cycles

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Conclusion: The Future of AI-Assisted Development

The seven strategies outlined here represent a fundamental shift in how we approach software development. By mastering context-rich prompts, leveraging AI for refactoring and testing, and customizing AI behavior, you can achieve productivity gains that were unimaginable just a few years ago.

Key Takeaways

1. Invest time in prompt engineering—a well-crafted prompt saves hours of iteration
2. Use AI for the boring stuff—refactoring, testing, and documentation are perfect AI tasks
3. Chain workflows—don't treat each step as isolated; build on previous outputs
4. Customize with system prompts—set the AI's persona to match your needs
5. Always verify—AI is a powerful assistant, but human judgment remains essential

What's Next?

As AI models continue to improve, the line between "AI-assisted" and "AI-automated" development will blur. The developers who thrive will be those who learn to collaborate effectively with these tools, treating them as junior partners who can handle 80% of the work, leaving humans to focus on architecture, design, and innovation.

Start implementing these strategies today. Your future self—with 50-90% more free time—will thank you.