Kling AI Known Pitfalls

Overview

This skill documents common mistakes, gotchas, and pitfalls when working with Kling AI, along with solutions and best practices to avoid them.

Prerequisites

• Basic Kling AI usage experience
• Encountered issues to troubleshoot
• Desire to improve implementation

Instructions

Follow these steps to avoid pitfalls:

1. Review Common Issues: Understand frequent problems
2. Apply Best Practices: Implement recommendations
3. Test Thoroughly: Validate implementations
4. Monitor Continuously: Watch for new issues
5. Update Regularly: Keep up with API changes

Common Pitfalls

1. Not Handling Async Jobs

# WRONG: Assuming immediate result
def bad_generate():
    response = requests.post(
        "https://api.klingai.com/v1/videos/text-to-video",
        json={"prompt": "test"}
    )
    # This returns job_id, NOT video_url!
    video_url = response.json()["video_url"]  # KeyError!

# RIGHT: Poll for completion
def good_generate():
    response = requests.post(
        "https://api.klingai.com/v1/videos/text-to-video",
        json={"prompt": "test"}
    )
    job_id = response.json()["job_id"]

    # Poll until complete
    while True:
        status = requests.get(f"https://api.klingai.com/v1/videos/{job_id}")
        data = status.json()

        if data["status"] == "completed":
            return data["video_url"]
        elif data["status"] == "failed":
            raise Exception(data.get("error"))

        time.sleep(5)

2. Ignoring Rate Limits

# WRONG: Rapid fire requests
def bad_batch():
    for prompt in prompts:
        # Will hit rate limit quickly
        requests.post(url, json={"prompt": prompt})

# RIGHT: Respect rate limits
def good_batch():
    from ratelimit import limits, sleep_and_retry

    @sleep_and_retry
    @limits(calls=60, period=60)  # 60 requests per minute
    def rate_limited_generate(prompt):
        return requests.post(url, json={"prompt": prompt})

    for prompt in prompts:
        rate_limited_generate(prompt)

3. Not Handling Errors

# WRONG: No error handling
def bad_client():
    response = requests.post(url, json={"prompt": prompt})
    return response.json()  # Will crash on error

# RIGHT: Comprehensive error handling
def good_client():
    try:
        response = requests.post(url, json={"prompt": prompt}, timeout=30)
        response.raise_for_status()
        return response.json()
    except requests.exceptions.Timeout:
        print("Request timed out - retry later")
        raise
    except requests.exceptions.HTTPError as e:
        if e.response.status_code == 429:
            retry_after = int(e.response.headers.get("Retry-After", 60))
            print(f"Rate limited - wait {retry_after}s")
        elif e.response.status_code == 401:
            print("Invalid API key")
        elif e.response.status_code == 400:
            print(f"Bad request: {e.response.json()}")
        raise
    except requests.exceptions.RequestException as e:
        print(f"Network error: {e}")
        raise

4. Hardcoding API Keys

# WRONG: Hardcoded secret
API_KEY = "klingai_sk_1234567890"  # Never do this!

# RIGHT: Environment variable
API_KEY = os.environ.get("KLINGAI_API_KEY")
if not API_KEY:
    raise ValueError("KLINGAI_API_KEY environment variable not set")

# BETTER: Secrets manager
from google.cloud import secretmanager

def get_api_key():
    client = secretmanager.SecretManagerServiceClient()
    name = "projects/my-project/secrets/klingai-api-key/versions/latest"
    response = client.access_secret_version(request={"name": name})
    return response.payload.data.decode("UTF-8")

5. Not Validating Prompts

# WRONG: Pass any prompt directly
def bad_generate(user_prompt):
    return api.generate(prompt=user_prompt)  # Could be anything!

# RIGHT: Validate and sanitize prompts
def good_generate(user_prompt):
    # Length check
    if len(user_prompt) < 10:
        raise ValueError("Prompt too short - needs more detail")
    if len(user_prompt) > 2000:
        raise ValueError("Prompt too long - please shorten")

    # Content check
    prohibited = ["violence", "explicit", "illegal"]
    for word in prohibited:
        if word in user_prompt.lower():
            raise ValueError(f"Prohibited content detected: {word}")

    # Sanitize
    cleaned_prompt = user_prompt.strip()
    cleaned_prompt = " ".join(cleaned_prompt.split())  # Normalize whitespace

    return api.generate(prompt=cleaned_prompt)

6. Ignoring Video Expiration

# WRONG: Store URL and assume it works forever
def bad_storage():
    result = generate_video(prompt)
    database.save(video_url=result["video_url"])  # URL will expire!

# RIGHT: Download and store video
def good_storage():
    result = generate_video(prompt)
    video_url = result["video_url"]

    # Download immediately
    response = requests.get(video_url)
    video_content = response.content

    # Store in permanent storage
    s3.put_object(
        Bucket="my-bucket",
        Key=f"videos/{result['job_id']}.mp4",
        Body=video_content
    )

    # Save permanent URL
    database.save(
        video_url=f"https://my-bucket.s3.amazonaws.com/videos/{result['job_id']}.mp4",
        original_job_id=result["job_id"]
    )

7. Blocking Main Thread

# WRONG: Block while waiting
def bad_sync():
    job_id = submit_job(prompt)
    while True:
        status = check_status(job_id)
        if status == "completed":
            break
        time.sleep(5)  # Blocks entire application

# RIGHT: Async or background processing
import asyncio

async def good_async():
    job_id = await submit_job(prompt)

    while True:
        status = await check_status(job_id)
        if status == "completed":
            break
        await asyncio.sleep(5)  # Yields to event loop

# Or use webhooks
def good_webhook():
    job_id = submit_job(prompt, webhook_url="https://myapp.com/webhook")
    # Don't wait - webhook will notify when done

8. Not Tracking Costs

# WRONG: Unlimited spending
def bad_unlimited():
    for prompt in prompts:  # Could be thousands!
        generate_video(prompt)

# RIGHT: Budget controls
def good_budget_controlled():
    DAILY_BUDGET = 100  # Credits
    used_today = get_usage_today()

    for prompt in prompts:
        cost_estimate = estimate_cost(prompt)

        if used_today + cost_estimate > DAILY_BUDGET:
            print(f"Budget exceeded: {used_today}/{DAILY_BUDGET}")
            break

        generate_video(prompt)
        used_today += cost_estimate

9. Poor Prompt Engineering

# WRONG: Vague prompt
bad_prompt = "a video"

# WRONG: Too complex
bad_prompt = "a hyper-realistic 8K video of a dragon fighting a robot in a futuristic city with explosions and lens flares and dramatic lighting and cinematic camera movements and..."

# RIGHT: Clear and specific
good_prompt = """
A peaceful forest clearing at dawn.
Sunlight filters through the trees.
Gentle fog drifts across the ground.
Cinematic, warm lighting, calm mood.
"""

10. Not Testing Before Production

# WRONG: Deploy untested
def bad_deploy():
    # Hope it works in production!
    deploy_to_production(video_service)

# RIGHT: Test thoroughly
def good_deploy():
    # Unit tests
    def test_prompt_validation():
        assert validate_prompt("test") == False
        assert validate_prompt("valid prompt here") == True

    # Integration test with sandbox
    def test_sandbox_generation():
        result = generate_video(
            prompt="Test video",
            environment="sandbox"
        )
        assert result["status"] == "completed"

    # Load test
    def test_rate_limits():
        for i in range(100):
            try:
                generate_video(f"Test {i}")
            except RateLimitError:
                assert i >= 60  # Expected to hit limit

    # Run all tests before deploy
    test_prompt_validation()
    test_sandbox_generation()
    test_rate_limits()

    deploy_to_production(video_service)

Quick Reference Checklist

Before Going Live:

[ ] API key stored securely (not in code)
[ ] Error handling for all API calls
[ ] Rate limiting implemented
[ ] Async job handling (polling or webhooks)
[ ] Prompt validation
[ ] Video download/storage (URLs expire)
[ ] Budget controls
[ ] Logging and monitoring
[ ] Test coverage
[ ] Fallback handling

Common Error Codes:
- 400: Bad request (check prompt/params)
- 401: Invalid API key
- 403: Insufficient permissions
- 429: Rate limit exceeded
- 500: Server error (retry)
- 503: Service unavailable (retry later)

Output

Successful execution produces:

• Robust error handling
• Proper async patterns
• Secure credential management
• Cost-controlled generation

Error Handling

This entire skill is about error handling - apply the patterns shown above.

Examples

See code examples above for complete, runnable implementations.

Resources

• Kling AI Best Practices
• API Error Reference
• Security Guidelines