How to Prompt Gemini 2.0: Long Context, Multimodal, and Grounding

Gemini 2.0 is Google's flagship model, with some capabilities that genuinely stand out from the competition: a 1M-token context window, native multimodal input (text, image, audio, video), and built-in Google Search grounding. These are real advantages for specific use cases — but they require different prompting strategies than text-only models.

What Sets Gemini Apart

The 1M-token context window. Gemini 1.5 and 2.0 have the largest context windows of any widely available model. This enables workflows that would be impossible elsewhere: analyzing an entire large codebase in one pass, processing hours of video transcript, or synthesizing hundreds of research documents simultaneously.

Native multimodal input. Gemini processes text, images, audio, and video in a single unified model — not through separate systems. This enables richer reasoning across modalities. For example, you can ask it to correlate what's being said in a video with what's visible on screen.

Google Search grounding. The ability to anchor responses to live web search makes Gemini particularly strong for tasks requiring current information. When grounding is enabled, the model cites sources from real-time search rather than relying solely on training data.

Code execution. The Code Execution API lets Gemini write and run Python code to answer questions — useful for mathematical calculations, data analysis, and problems where computing a precise answer is better than estimating one.

Prompting for Long Context

Having a million-token window doesn't mean you should fill it randomly. The model's attention is not equally distributed across all tokens — structure matters.

Put the most important content early or at the very end. Research on long-context models shows a "lost in the middle" effect: models attend better to content at the start and end of the context than to content buried in the middle. If you have critical instructions or key documents, position them strategically.

Use clear delimiters and labels for long documents:

Below are three research papers you'll need to synthesize. Each is labeled
with its source.

=== PAPER 1: Stanford 2024 Study on Working Memory ===
[paper 1 content]

=== PAPER 2: MIT 2025 Replication ===
[paper 2 content]

=== PAPER 3: Meta-analysis, Journal of Cognitive Science ===
[paper 3 content]

---
TASK: Compare the methodologies of these three papers and identify where
their findings agree and conflict. Focus specifically on sample size
and measurement approach differences.

For code analysis, provide structure about what to look for:

Here is the full codebase for a Next.js application (approximately 50 files).
I need you to:
1. Identify all API routes and their HTTP methods
2. List all database queries and the tables they access
3. Find any potential N+1 query problems in the ORM calls

Repository structure is provided first, followed by file contents.
[repo contents]

Grounding With Google Search

Grounding is available through the Gemini API and makes the model reference real-time web results:

import google.generativeai as genai

genai.configure(api_key="your-api-key")
model = genai.GenerativeModel("gemini-2.0-flash")

# Enable grounding
response = model.generate_content(
    "What are the latest developments in fusion energy as of early 2026?",
    tools="google_search_retrieval"
)

print(response.text)
# Response cites real-time search results with sources

When to use grounding:

• Current events, news, recent research
• Product prices, availability, specifications
• Any question where the answer changes over time
• Fact-checking claims against current sources

When grounding adds little value:

• Timeless questions (math, programming concepts, history)
• Creative tasks
• Questions you want answered from training data specifically
• Low-latency production scenarios (grounding adds overhead)

Multimodal Prompting

Gemini handles multiple modalities natively. The key is being specific about what you want from each:

Image + text:

response = model.generate_content([
    "This is a screenshot of a production error in our web application. "
    "Identify: (1) the error type, (2) the likely root cause based on the stack trace, "
    "(3) the specific line of code most likely responsible, "
    "(4) a concrete fix. Be specific — don't say 'check your configuration.'",
    image  # PIL Image or bytes
])

Video analysis:

# Upload video file first
video_file = genai.upload_file(path="meeting_recording.mp4")

response = model.generate_content([
    "This is a 45-minute product meeting recording. "
    "Generate: (1) a 3-bullet summary of decisions made, "
    "(2) a list of action items with assigned owners (if mentioned), "
    "(3) any unresolved questions that need follow-up.",
    video_file
])

Audio transcription and analysis:

audio_file = genai.upload_file(path="customer_call.mp3")

response = model.generate_content([
    "This is a recorded customer support call. "
    "Transcribe the key parts where the customer describes their problem. "
    "Then categorize the issue type and identify the root cause based on the description.",
    audio_file
])

Code Execution

Gemini can write and execute Python code to answer questions that require computation:

model = genai.GenerativeModel(
    "gemini-2.0-flash",
    tools="code_execution"
)

response = model.generate_content(
    "I have a dataset of 1000 customer orders. The average order value is $85 "
    "with a standard deviation of $42. Assuming normal distribution, what percentage "
    "of orders are between $50 and $120? Show the calculation."
)

The model writes Python, executes it in a sandbox, and returns both the code and the precise computed answer. This is more reliable than asking the model to estimate mathematical results from memory.

Practical Settings

Common Mistakes With Gemini

Filling the context window without structure. A million tokens is only useful if the model can navigate them. Label your documents, provide a clear structure summary, and tell the model where to look.

Using grounding when you want deterministic answers. If your prompt is about timeless facts or training-data-dependent reasoning, grounding adds noise and latency without benefit.

Not specifying what to extract from images/video. "Analyze this video" gets a generic description. "Identify all customer objections raised in this sales call recording and categorize them by type" gets actionable output.

Ignoring Flash for production cost optimization. Gemini Flash handles most common tasks at significantly lower cost than Pro. Benchmark both before defaulting to Pro.