Gemini Prompt Engineering

Gemini is Google's multimodal AI model designed from the ground up for text, images, audio, video, and code. It features system instructions, ultra-long context windows (up to 1M+ tokens), and native multimodal understanding.

When to Invoke This Skill

Use this skill when:

• Crafting prompts specifically for Gemini/Google models

• Using system instructions to guide behavior

• Working with multimodal inputs (text, images, video, audio)

• Leveraging ultra-long context (1M+ tokens)

• Building with Gemini's agent reasoning capabilities

Gemini's Identity & Characteristics

Attribute Description

Architecture Multimodal-first (text, images, audio, video, code)

Context Window Up to 1M+ tokens (industry-leading)

System Instructions Primary feature for behavior control

Strengths Multimodal reasoning, long-context, code generation

Prompt Style Flexible with system instruction preference

Models Gemini 3 Flash (fast), Gemini 3 Pro (capable), Gemini 2.5 Flash/Pro (legacy)

Universal Prompting Techniques (Gemini-Adapted)

1. Zero-Shot Prompting with System Instructions

Gemini's system instructions are powerful for zero-shot tasks.

{ "system_instruction": { "parts": [{"text": "You are a technical writing assistant. Your responses are clear, concise, and use Markdown formatting."}] }, "contents": [{"parts": [{"text": "Explain how JWT authentication works."}]}] }

2. Few-Shot Prompting

<system_instruction> You are a sentiment classifier. Categorize text as positive, negative, or neutral. </system_instruction>

<examples> <example> <input> I absolutely love this product! Best purchase I've made all year. </input> <output> {"sentiment": "positive", "confidence": 0.95} </output> </example>

<example> <input> This is the worst customer service I've ever experienced. </input> <output> {"sentiment": "negative", "confidence": 0.92} </output> </example> </examples>

<input> The product is okay, does what it's supposed to do. </input>

<output>

3. Chain-of-Thought Prompting

<system_instruction> You are a strong reasoner. Always think through problems step by step before answering. </system_instruction>

The odd numbers in this group add up to an even number: 4, 8, 9, 15, 12, 2, 1.

Let's think about this systematically:

4. Zero-Shot CoT

Simply add reasoning instructions:

<system_instruction> Before answering, always think through the problem step by step. </system_instruction>

What's the capital of the country that has the largest population in South America?

Let's work through this step by step.

5. Prompt Chaining

Gemini's long context enables extensive chaining:

Chain 1:

<system_instruction> You are a research assistant. </system_instruction>

Extract all research papers related to "transformer architecture" from this document.

<document> [paste large document] </document>

Chain 2:

Summarize the key findings from the extracted papers and identify common themes.

<extracted_papers> [from previous response] </extracted_papers>

6. ReAct Prompting

<system_instruction> You are a reasoning agent. Before taking any action, analyze logical dependencies, constraints, and risks. Think through the problem methodically. </system_instruction>

<question> [research question] </question>

<thought_1> [analysis and plan] </thought_1>

<action_1> [tool use or information gathering] </action_1>

<observation_1> [result] </observation_1>

<thought_2> [next steps based on observation] </thought_2>

<final_answer> [conclusion] </final_answer>

7. Tree of Thoughts

<system_instruction> You are an expert planner. Explore multiple solution paths before recommending an approach. </system_instruction>

<problem> [complex problem] </problem>

<thought_paths> <path_1> <approach>[strategy 1]</approach> <reasoning>[step-by-step]</reasoning> <expected_outcome>[result]</expected_outcome> </path_1>

<path_2> [...] </path_2>

<path_3> [...] </path_3> </thought_paths>

<recommendation> [best approach with justification] </recommendation>

Gemini-Specific Best Practices

1. Use System Instructions

System instructions are Gemini's primary behavior control mechanism:

{ "system_instruction": { "parts": [{ "text": "You are a specialized assistant for data science. You are precise, analytical, and always provide code examples in Python." }] } }

2. Comprehensive System Instruction Template

From official Gemini documentation:

<role> You are Gemini, a specialized assistant for [Domain]. You are precise, analytical, and persistent. </role>

<instructions>

1. Plan: Analyze the task and create a step-by-step plan.
2. Execute: Carry out the plan.
3. Validate: Review your output against the user's task.
4. Format: Present the final answer in the requested structure. </instructions>

<constraints>

• Verbosity: [Low/Medium/High]
• Tone: [Formal/Casual/Technical] </constraints>

<output_format> Structure your response as follows:

1. Executive Summary: [Short overview]

2. Detailed Response: [The main content] </output_format>

3. Leverage Multimodal Inputs

Gemini natively processes multiple modalities:

{ "contents": [{ "parts": [ {"text": "Describe what's in this image and suggest a caption for social media."}, { "inline_data": { "mime_type": "image/jpeg", "data": "[base64_encoded_image]" } } ] }] }

4. Ultra-Long Context Utilization

Gemini's 1M+ token context enables massive document analysis:

<system_instruction> You are a document analysis specialist. </system_instruction>

<documents> [Hundreds of pages of content - up to 1M tokens] </documents>

<task> Synthesize key themes across all documents and identify contradictions. </task>

5. Code-Specific Prompting

Gemini excels at code generation and analysis:

<system_instruction> You are a senior software engineer. You provide clean, well-documented code with error handling. </system_instruction>

Write a Python function that:

1. Validates email addresses using regex
2. Returns (is_valid, error_message) tuple
3. Includes comprehensive docstring
4. Handles edge cases

Language: Python

Advanced Features

Function Calling

Gemini supports native function/tool calling for building agents:

from google import genai from google.genai import types

client = genai.Client()

get_weather = types.FunctionDeclaration( name="get_weather", description="Get current weather for a location", parameters=types.Schema( type=types.Type.OBJECT, properties={ "location": types.Schema( type=types.Type.STRING, description="City name, e.g. San Francisco" ), "unit": types.Schema( type=types.Type.STRING, description="Temperature unit (celsius or fahrenheit)", enum=["celsius", "fahrenheit"] ) }, required=["location"] ) )

response = client.models.generate_content( model="gemini-2.5-flash", contents="What's the weather in Tokyo and Paris?", config=types.GenerateContentConfig( tools=[get_weather] ) )

Thinking Configuration

Control Gemini's reasoning process with configurable thinking budget:

response = client.models.generate_content( model="gemini-2.5-flash", contents="Solve this step-by-step: [complex problem]", config=types.GenerateContentConfig( thinking_config=types.ThinkingConfig( thinking_budget=8192 # tokens for reasoning ) ) )

Structured Outputs with JSON Schema

Get validated JSON output with schema enforcement:

response = client.models.generate_content( model="gemini-2.5-flash", contents="Extract user profile information from this text: [text]", config=types.GenerateContentConfig( response_mime_type="application/json", response_schema=types.Schema( type=types.Type.OBJECT, properties={ "name": types.Schema(type=types.Type.STRING), "email": types.Schema(type=types.Type.STRING), "age": types.Schema(type=types.Type.INTEGER), "interests": types.Schema( type=types.Type.ARRAY, items=types.Schema(type=types.Type.STRING) ) }, required=["name", "email"] ) ) )

Anti-Patterns to Avoid

Anti-Pattern Why It Fails Better Approach

Ignoring system instructions Wastes Gemini's key feature Always set system_instruction

Not using multimodal Underutilizes Gemini's strength Combine text, images, audio

Small context thinking Wastes 1M+ capability Process large documents

Inconsistent formats Confuses multimodal processing Specify output format clearly

Single-shot for complex tasks Misses reasoning depth Use multi-turn conversations

Not using structured outputs Manual parsing needed Use JSON schema validation

Disabling thinking when needed Misses reasoning insights Enable thinking_budget for complex tasks

Quick Reference Templates

Basic System Instruction

{ "system_instruction": { "parts": [{"text": "[Your system instruction here]"}] }, "contents": [{"parts": [{"text": "[Your prompt]"}]}] }

Multimodal Input

<system_instruction> You are a visual analysis assistant. </system_instruction>

Analyze this image and describe:

1. Main subject
2. Mood/atmosphere
3. Suggested use cases

[image]

Long-Context Analysis

<system_instruction> You are a research analyst specializing in synthesis and pattern recognition. </system_instruction>

<large_context> [up to 1M tokens of content] </large_context>

<task> [analysis task] </task>

<output_format> [structure] </output_format>

Model Capabilities Reference

Feature Gemini 3 Flash Gemini 3 Pro Gemini 2.5 Flash Gemini 2.5 Pro

Context Window 1M tokens 1M tokens 1M tokens 1M tokens

System Instructions ✅ Yes ✅ Yes ✅ Yes ✅ Yes

Multimodal ✅ Native ✅ Native ✅ Native ✅ Native

Code ✅ Excellent ✅ Excellent ✅ Excellent ✅ Excellent

Reasoning ✅ Strong ✅ Excellent ✅ Strong ✅ Excellent

Speed Very Fast Fast Very Fast Fast

Function Calling ✅ Yes ✅ Yes ✅ Yes ✅ Yes

Thinking Config ✅ Yes ✅ Yes ✅ Yes ✅ Yes

Status Latest (2025) Latest (2025) Mature Mature

Note: Model names follow gemini-{version}-{variant} pattern. Use gemini-3-flash-preview for the latest features.

System Instruction Patterns

Role Definition

You are a [role] specializing in [domain]. You are [attribute 1], [attribute 2], and [attribute 3].

Task Instructions

When given a task:

1. Analyze: Break down requirements
2. Plan: Create step-by-step approach
3. Execute: Complete the task
4. Review: Verify against requirements

Output Formatting

Always structure your responses as:

• Summary: Brief overview
• Details: Main content
• Examples: Concrete illustrations (if applicable)
• Caveats: Limitations or considerations

Behavioral Constraints

• Always cite sources when making factual claims
• Indicate confidence levels for uncertain information
• Offer alternative viewpoints on subjective topics
• Flag potential ethical concerns

See Also

• references/basics.md

• Foundational Gemini prompting concepts

• references/techniques.md

• Detailed technique explanations

• references/system-instructions.md

• System instruction patterns

• references/multimodal.md

• Multimodal prompting guide

• references/patterns.md

• Reusable Gemini prompt patterns

• references/examples.md

• Concrete examples and templates

• grok-prompting skill - For Grok/xAI-specific guidance

• claude-prompting skill - For Anthropic Claude-specific guidance