Gemini Best Practices

Learn to use Gemini safely and effectively. Master safety settings, cost optimization, API best practices, rate limit management, and responsible AI use.

Safety Settings

Gemini includes configurable safety filters that block content across several categories. Understanding and properly configuring these is essential for production applications:

import google.generativeai as genai

safety_settings = [
    {
        "category": "HARM_CATEGORY_HARASSMENT",
        "threshold": "BLOCK_MEDIUM_AND_ABOVE"
    },
    {
        "category": "HARM_CATEGORY_HATE_SPEECH",
        "threshold": "BLOCK_MEDIUM_AND_ABOVE"
    },
    {
        "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
        "threshold": "BLOCK_LOW_AND_ABOVE"
    },
    {
        "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
        "threshold": "BLOCK_MEDIUM_AND_ABOVE"
    }
]

model = genai.GenerativeModel(
    'gemini-pro',
    safety_settings=safety_settings
)

Cost Optimization

Managing API costs effectively is crucial for sustainable use of Gemini:

1. Choose the Right Model

   Use Flash for development and testing. Only upgrade to Pro or Ultra when the quality difference justifies the cost increase. Flash is 10-20x cheaper than Pro.

2. Minimize Token Usage

   Keep prompts concise. Remove unnecessary context. Use system instructions to set behavior once rather than repeating in every prompt.

3. Set Max Output Tokens

   Always set max_output_tokens to prevent unexpectedly long responses. If you need a one-line answer, set it to 100 tokens.

4. Cache Repeated Context

   Use Gemini's context caching feature for conversations where the same large context (like a document) is referenced repeatedly.

5. Batch Similar Requests

   Group multiple small tasks into a single prompt when possible. Processing 10 items in one call is cheaper than 10 separate calls.

6. Monitor Usage

   Set up billing alerts in Google Cloud Console. Track per-endpoint costs and optimize the most expensive operations first.

API Best Practices

Error Handling

import google.generativeai as genai
import time

def generate_with_retry(prompt, max_retries=3):
    for attempt in range(max_retries):
        try:
            model = genai.GenerativeModel('gemini-pro')
            response = model.generate_content(prompt)

            # Check if response was blocked
            if response.prompt_feedback.block_reason:
                print(f"Blocked: {response.prompt_feedback}")
                return None

            return response.text

        except Exception as e:
            if "429" in str(e):
                # Rate limited - wait and retry
                wait = 2 ** attempt
                print(f"Rate limited. Waiting {wait}s...")
                time.sleep(wait)
            else:
                raise

    return None

Rate Limits

Understanding and respecting rate limits prevents service disruptions:

Responsible Use

Using AI responsibly is essential. Follow these guidelines:

• Verify outputs: Always review Gemini's responses for accuracy, especially for factual claims, medical advice, legal guidance, or financial recommendations.
• Disclose AI use: Be transparent when content is AI-generated, especially in professional and academic contexts.
• Protect privacy: Never send personally identifiable information (PII), passwords, API keys, or confidential data to the API without proper safeguards.
• Avoid bias amplification: Be aware that AI models can reflect and amplify societal biases. Review outputs for fairness and balanced representation.
• Follow terms of service: Adhere to Google's Acceptable Use Policy and Generative AI Prohibited Use Policy.
• Human oversight: Keep humans in the loop for important decisions. AI should augment human judgment, not replace it.

Frequently Asked Questions