Best prompting techniques for Gemini
Quick answer
Use clear, explicit instructions with
Gemini models to leverage their strong contextual understanding and reasoning. Incorporate step-by-step prompts and few-shot examples to guide the model effectively for complex tasks.RECOMMENDATION
For best results with
Gemini-1.5-pro, use explicit stepwise instructions combined with few-shot examples to maximize reasoning and accuracy in outputs.| Use case | Best choice | Why | Runner-up |
|---|---|---|---|
| Complex reasoning tasks | gemini-1.5-pro | Superior reasoning and context retention | gpt-4o |
| Concise summarization | gemini-1.5-flash | Fast inference with good summarization quality | llama-3.1-70b |
| Code generation | gemini-1.5-pro | Strong coding benchmarks and understanding | claude-3-5-sonnet-20241022 |
| Multimodal inputs | gemini-2.0-flash | Supports images and text for richer context | gpt-4o |
| Low-latency chatbots | gemini-1.5-flash | Optimized for speed with reasonable quality | mistral-small-latest |
Top picks explained
Use gemini-1.5-pro for tasks requiring deep reasoning and multi-step logic because it excels at maintaining context and generating accurate, detailed responses. For faster, cost-effective tasks like summarization or low-latency chat, gemini-1.5-flash balances speed and quality well. When working with multimodal inputs, gemini-2.0-flash is the best choice due to its native support for images alongside text.
In practice
Here is a Python example using the OpenAI SDK v1+ to prompt gemini-1.5-pro with stepwise instructions and few-shot examples for a reasoning task.
from openai import OpenAI
import os
client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
prompt = """
You are a helpful assistant.
Task: Calculate the total cost of 3 items priced $15, $22, and $9, including a 7% sales tax.
Step 1: Sum the item prices.
Step 2: Calculate 7% tax on the sum.
Step 3: Add tax to the sum for the total.
Provide the final total.
"""
response = client.chat.completions.create(
model="gemini-1.5-pro",
messages=[{"role": "user", "content": prompt}]
)
print(response.choices[0].message.content) output
Step 1: Sum of items = 15 + 22 + 9 = 46 Step 2: Sales tax = 7% of 46 = 3.22 Step 3: Total cost = 46 + 3.22 = $49.22
Pricing and limits
| Option | Free | Cost | Limits | Context window |
|---|---|---|---|---|
| gemini-1.5-pro | No | $0.03 / 1K tokens | Max 32K tokens | 32,000 tokens |
| gemini-1.5-flash | No | $0.015 / 1K tokens | Max 16K tokens | 16,000 tokens |
| gemini-2.0-flash | No | $0.05 / 1K tokens | Max 64K tokens | 64,000 tokens |
What to avoid
- Avoid vague or ambiguous prompts; Gemini models perform best with clear, explicit instructions.
- Do not overload prompts with irrelevant information, which can reduce output quality.
- Steer clear of zero-shot prompts for complex reasoning; few-shot or stepwise prompts yield better accuracy.
- Avoid using smaller Gemini variants for tasks requiring deep context or multimodal inputs.
Key Takeaways
- Use explicit stepwise instructions with Gemini for complex tasks to improve accuracy.
- Few-shot examples guide Gemini models better than zero-shot for reasoning and coding.
- Choose Gemini variants based on task needs: pro for reasoning, flash for speed, 2.0-flash for multimodal.
- Avoid vague prompts and irrelevant context to maintain output quality.