Google Gemini: Prompting Tips and Tricks

Google's Gemini models have distinct strengths that set them apart from Claude and GPT-4: a 2-million-token context window, native multimodal input (text, images, audio, video, code), grounding with Google Search, and deep integration with Google Workspace. This guide covers how to use these capabilities effectively.

The Gemini Model Family

Choose the right model for your task:

For most prompting use cases, Gemini 2.5 Pro gives the best results on complex tasks. Flash is the right choice when speed and cost matter more than maximum quality.

Multimodal Prompting

Gemini was built multimodal from the ground up — images, audio, video, and code are first-class inputs, not add-ons.

Images

[Attach image]
"Analyze this architecture diagram. Identify:
1. Any single points of failure
2. Missing redundancy
3. Security boundary concerns
Format as a bulleted list by severity."

[Attach product photo]
"Write 3 product description variants for this item:
- One for an Amazon listing (feature-focused)
- One for Instagram (aspirational, short)
- One for a technical catalog (specs-focused)"

Video

Gemini 1.5 Pro and 2.0 can process video directly — up to 1 hour of video in the context window:

[Attach video file]
"Create a timestamped summary of this meeting recording.
For each agenda item, note:
- Decision made (or not)
- Action items and owners
- Any unresolved disagreements"

[Attach tutorial video]
"Extract all code shown in this tutorial and organize it
into a single working script. Add comments explaining
what each section does."

Audio

[Attach audio file]
"Transcribe this interview. Format as:
Speaker labels (Speaker 1, Speaker 2) with timestamps.
Highlight any technical terms that may need verification."

Documents at Scale

Gemini's 2M token context window means you can analyze entire books, large codebases, or complete document archives:

[Attach 300-page PDF]
"This is a legal contract. Find all clauses related to:
- Intellectual property ownership
- Termination conditions
- Liability caps
Cite the section number and page for each finding."

Grounding with Google Search

Grounding connects Gemini responses to current web information. It's Gemini's equivalent of ChatGPT's browsing — but integrated more deeply.

Enabling Grounding

Via the API:

import google.generativeai as genai

model = genai.GenerativeModel("gemini-2.5-pro")
response = model.generate_content(
    "What are the latest benchmark results for Gemini 2.5 Pro on coding tasks?",
    tools="google_search_retrieval"
)

In AI Studio or Gemini.google.com: the "Google it" toggle enables grounding.

When to Use Grounding

Use grounding for:

• Current events and recent news
• Up-to-date prices, availability, or statistics
• Recent product releases or software updates
• Fact-checking claims that may have changed

Don't use it for:

• Tasks where you want the model to reason from provided context only
• Sensitive prompts where you don't want external data
• When you need deterministic, source-controlled responses

Grounding Prompt Patterns

"Search for current pricing for AWS us-east-1 EC2 t3.medium instances
and compare to the equivalent GCP and Azure VM sizes. Current as of today."

"Research the current state of the Rust async ecosystem.
Focus on: which runtime is most widely used, current controversy or
debate in the community, and any major releases in the last 6 months."

Long Context Best Practices

Gemini's 2M token window is a powerful capability but requires thoughtful use.

Position Your Key Questions Strategically

For very long documents, put critical questions at both the beginning and end of your prompt. Research shows model attention degrades in the middle of very long contexts ("lost in the middle" phenomenon).

[State question clearly]

[Paste 500k tokens of document]

[Restate the specific question you want answered, referencing
key section names or topics]

Chunk and Summarize for Extreme Length

For tasks where you need to process more content than fits even in Gemini's context:

"I'm going to send you this document in 5 sections.
After each section, respond only with: 'Section [N] received. Key points: [3 bullet points].'
After all sections, I'll ask for the final analysis.

Section 1 of 5:
[content]"

Explicit Section References

"The document below is organized into:
- Executive Summary (pages 1-2)
- Technical Architecture (pages 3-15)
- Implementation Timeline (pages 16-20)
- Appendices (pages 21+)

Focus your analysis exclusively on the Technical Architecture section.
[document]"

Structured Output

Gemini supports native JSON schema output — the model is constrained to return valid JSON matching your schema.

import google.generativeai as genai
import json

model = genai.GenerativeModel("gemini-2.5-pro")

schema = {
    "type": "object",
    "properties": {
        "entities": {
            "type": "array",
            "items": {
                "type": "object",
                "properties": {
                    "name": {"type": "string"},
                    "type": {"type": "string", "enum": ["person", "organization", "location"]},
                    "mentions": {"type": "integer"}
                }
            }
        }
    }
}

response = model.generate_content(
    f"Extract all named entities from this text: {text}",
    generation_config=genai.GenerationConfig(
        response_mime_type="application/json",
        response_schema=schema
    )
)

Code Execution

Gemini can execute Python code in a sandboxed environment — useful for data analysis and verification:

"Analyze this CSV data. Run the analysis in code and show me:
1. Summary statistics for all numeric columns
2. A correlation matrix
3. Any outliers (values beyond 3 standard deviations)
[paste CSV]
Execute the code and show me both the code and results."

Gemini in Google Workspace

For users in Google Workspace:

• Gmail: Summarize long email threads; draft replies
• Docs: "Help me write" with context from the document
• Sheets: Analyze data, create formulas from natural language
• Slides: Generate presentations from outlines

The Workspace integration has access to your documents as context — useful for personalized prompts:

"Based on the project proposal in my Drive [link], draft a follow-up
email to the client summarizing next steps."

Key Takeaways

• Gemini's multimodal capabilities are native — use images, video, and audio directly
• Grounding with Google Search gives access to current information
• The 2M token context window handles entire codebases or document archives
• For very long contexts, position key questions at both start and end
• Use native JSON schema output for structured data extraction