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Introduction

Generative AI on Google Cloud enables organizations to transform their workflows across industries. This section showcases real-world use cases with working code examples from production implementations.

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Use Case Categories

Our use cases span multiple domains, each demonstrating practical applications of Gemini and other generative AI models:

Document Processing

Extract, classify, summarize, and analyze documents with multimodal AI

Customer Support

Build intelligent chatbots and automate support workflows

Code Generation

Accelerate development with AI-powered coding assistance

Data Analytics

Generate SQL, extract insights, and automate analysis with BigQuery

Education

Create personalized learning experiences and educational content

Healthcare

Improve medical coding, clinical documentation, and patient care

Marketing

Generate compelling content and personalized campaigns

Finance

Automate KYC, risk analysis, and financial document processing

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Common Implementation Patterns

Across these use cases, several powerful patterns emerge:

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Multimodal Understanding

Gemini’s native multimodality enables processing of text, images, audio, video, and PDFs in a single request: 
from google import genai
from google.genai.types import Part

client = genai.Client(vertexai=True, project=PROJECT_ID, location=LOCATION)

response = client.models.generate_content(
    model="gemini-2.0-flash",
    contents=[
        "Analyze this document and image:",
        Part.from_uri(file_uri="gs://bucket/document.pdf", mime_type="application/pdf"),
        Part.from_uri(file_uri="gs://bucket/image.jpg", mime_type="image/jpeg"),
    ],
)

print(response.text)

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Structured Output

Control generation format using Pydantic models or JSON schemas: 
from pydantic import BaseModel
from google.genai.types import GenerateContentConfig

class ProductInfo(BaseModel):
    name: str
    price: float
    category: str
    in_stock: bool

response = client.models.generate_content(
    model="gemini-2.0-flash",
    contents="Extract product information from this description...",
    config=GenerateContentConfig(
        response_schema=ProductInfo,
        response_mime_type="application/json",
    ),
)

product = response.parsed  # Pydantic model instance

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Function Calling

Extend model capabilities by connecting to external tools and APIs: 
from google.genai.types import FunctionDeclaration, Tool

get_weather = FunctionDeclaration(
    name="get_weather",
    description="Get current weather for a location",
    parameters={
        "type": "OBJECT",
        "properties": {
            "location": {"type": "STRING", "description": "City name"}
        },
    },
)

tool = Tool(function_declarations=[get_weather])

response = client.models.generate_content(
    model="gemini-2.0-flash",
    contents="What's the weather in San Francisco?",
    config=GenerateContentConfig(tools=[tool]),
)

if response.function_calls:
    for call in response.function_calls:
        print(f"Function: {call.name}")
        print(f"Args: {call.args}")

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Retrieval Augmented Generation (RAG)

Ground responses in your own data using vector search: 
from langchain.vectorstores import FAISS
from langchain_google_vertexai import VertexAIEmbeddings

# Create embeddings and vector store
embeddings = VertexAIEmbeddings(model_name="text-embedding-005")
db = FAISS.from_documents(documents, embeddings)

# Retrieve relevant context
retriever = db.as_retriever(search_kwargs={"k": 5})
relevant_docs = retriever.get_relevant_documents(query)

# Generate with context
context = "\n".join([doc.page_content for doc in relevant_docs])
response = client.models.generate_content(
    model="gemini-2.0-flash",
    contents=f"Context: {context}\n\nQuestion: {query}",
)
Enable real-time web search for up-to-date information: 
from google.genai.types import GoogleSearch, Tool

search_tool = Tool(google_search=GoogleSearch())

response = client.models.generate_content(
    model="gemini-2.0-flash",
    contents="What are the latest developments in quantum computing?",
    config=GenerateContentConfig(tools=[search_tool]),
)

print(response.text)
for chunk in response.candidates[0].grounding_metadata.search_entry_point.rendered_content:
    print(f"Source: {chunk}")

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Best Practices

1

Start with the Right Model

Use gemini-2.0-flash for fast, cost-effective tasks and gemini-2.5-pro for complex reasoning
2

Leverage System Instructions

Define clear system instructions to guide model behavior consistently
3

Use Structured Output

Constrain responses with schemas for reliable downstream processing
4

Implement Error Handling

Handle rate limits, timeouts, and validation errors gracefully
5

Monitor and Evaluate

Track model performance, costs, and user satisfaction metrics

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Next Steps

Explore specific use cases to see complete implementations:
All examples are drawn from the Google Cloud Generative AI repository with working notebooks you can run in Colab or Vertex AI Workbench.

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