Generative AI can write poetry, summarize documents, and generate code, but when it comes to real-world accuracy, things get tricky. LLMs are creative by design. They guess. They approximate. And sometimes… they hallucinate. The fix is simple and powerful: grounding.

Grounding is the process of connecting AI outputs to verifiable, real-time information. Instead of letting the model “guess,” grounding anchors it to factual sources so the output becomes reliable enough for real-world decisions. And when your use case involves anything in the physical world, shops, neighborhoods, routes, restaurants, businesses, or local recommendations, nothing beats Grounding with Google Maps.

Google recently made Grounding with Google Maps available through both the Gemini API and Vertex AI, and it has quickly become one of the most impactful capabilities for building trustworthy, location-aware AI applications.

🎥 Prefer watching instead of reading? You can watch the NotebookLM podcast video with slides and visuals based on this blog here.

1. Grounding: Why It Matters More Than Ever

Grounding is the process of connecting AI responses to verifiable, real-time sources of truth. Because LLMs rely on historical training data, they need live, authoritative information to stay accurate.

Grounding solves four core problems:

• Accuracy & Completeness: LLMs generate answers based on patterns in their training data. Grounding replaces assumptions with real, up-to-date information, ensuring the model’s responses are accurate rather than approximate.
• Freshness: Models do not know about yesterday’s store closure or a newly opened café. Grounding provides live data, so your AI reflects the world as it exists now, not as it was when the model was last trained.
• Explainability & Trust: Grounded responses come with citations. Users can see exactly where the information came from, making the model’s reasoning clear and reliable.
• Domain-Specific Augmentation: Enterprises can use grounding to enhance model responses by combining internal datasets with trusted external sources, unlocking personalized, domain-specific experiences that a general LLM could never generate on its own.

🌟 In short, grounding is the difference between “sounds right” and is right.

2. Grounding with Google Maps: A Local Expert Tool

Grounding with Google Maps brings together the reasoning power of Gemini models with Google’s rich, factual, and continuously updated geospatial data. This instantly turns your AI into a reliable local expert, capable of understanding the world through real locations, real businesses, and real user activity.

With access to over 250 million places globally, developers can build AI applications that respond with accurate, location-aware information. Just like Grounding with Google Search, this feature supplies the model with fresh, trustworthy data whenever a query includes a geographic component. Your AI does not guess; it grounds its answers in structured details such as addresses, opening hours, photos, user reviews, and ratings.

Grounding with Google Maps in the Gemini API

Grounding with Google Maps is available directly through the generateContent method in the Gemini API, making it easy to bring this capability into your applications.

from google import genai from google.genai.types import GoogleMaps,Tool, GenerateContentConfig client = genai.Client() response = client.models.generate_content(     model="gemini-2.5-flash",     contents="Where can I get the best espresso near me?",     config=GenerateContentConfig(         tools=[Tool(google_maps=GoogleMaps())], # Use Google Maps Tool         tool_config=types.ToolConfig(             retrieval_config = types.RetrievalConfig(                 lat_lng = types.LatLng(                     latitude=40.7128,                     longitude=-74.006         ))),     ), ) print(response.text) # Example response: 'Here are some of the top-rated places to get espresso near you: ...'

How It Works

The flow is simple and feels natural:

1. User Query: The user asks something with geographic context (for example, “museums in Istanbul”). The model immediately recognizes the need for location-based information.
2. Tool Invocation: Gemini automatically calls the Grounding with Google Maps tool. Developers can also pass the user’s latitude and longitude to refine and localize results.
3. Data Retrieval: The Grounding service queries Google Maps and returns real place data, reviews, photos, addresses, operating hours, and other attributes that provide context and accuracy.
4. Grounded Generation: Gemini uses this fresh Maps data to generate a response that is relevant, factual, and aligned with the real world.
5. Response & Context Token: The model returns the final grounded response along with citations (groundingChunks). And when enableWidget:true is included in the request, you also receive a googleMapsWidgetContextToken, allowing you to render an interactive Google Maps widget directly in your app’s UI.

💡 Check out the demo below to see how the Gemini API uses Google Maps grounding. If you want to explore it hands-on, click here.

3. Grounding with Google Maps in Vertex AI: Advanced Use Cases That Matter

Grounding with Google Maps is now GA in Vertex AI, unlocking advanced enterprise-grade scenarios across Travel, Retail, Real Estate, Logistics, Transportation, and Smart City applications.

Here are the most valuable features:

• Fresh, real-time place insights: Your application can instantly reflect current conditions, temporary closures, updated opening hours, or new reviews, something an offline LLM simply cannot do. This is essential for travel apps, customer assistants, and retail finders.
• Personalized, hyper-local recommendations: Grounding uses attributes like distance, accessibility, popularity, and ratings to deliver recommendations that match user intent. A travel app, for example, can build a full-day itinerary that respects walking distance, open hours, and nearby attractions.
• Domain-specific summaries powered by both Maps + your data: Industries like Real Estate get huge value here. A property summary can adapt based on the customer profile: families see nearby parks and schools, while young professionals get cafés and nightlife.
• Context-aware reasoning for operational tasks: Field teams, logistics apps, and delivery systems benefit from grounded geospatial data that allows AI to reason about routes, proximity, and location constraints.
• Accessibility-first experiences: By grounding with Maps and pairing it with voice-based interactions, applications can serve users who prefer or require voice input. This makes location-aware assistants more inclusive, offering both text and conversational voice experiences that accommodate everyone.

🌟 Grounding is not just about places; it is about enabling AI to think with real-world context.

4. Combining Maps Grounding with Search Grounding

For the most complete real-world understanding, developers can combine Grounding with Google Maps and Grounding with Google Search in a single request.

This pairing covers both sides of the real world:

• Maps give factual, structured place data.
• Search brings in descriptive and timely context from across the web.

So when a user asks about live events, opening times, or trending locations, the AI can reason with not just where something is, but what is happening there. It can use Maps for hours and location, and search for event schedules or news, producing responses that feel comprehensive and reassuringly up-to-date.

5. Real-World Use Cases: Where Grounding with Google Maps Truly Shines

Grounding with Google Maps unlocks powerful, factual, and location-aware experiences across multiple industries. Here are the scenarios where this capability makes a measurable difference:

1. Travel & Tourism: Smarter Trip Planning from Start to Finish Travel apps instantly level up with grounded geospatial intelligence. Detailed planning: Gemini can build a full day’s plan that respects walking distance, live business hours, and real travel times. No more outdated suggestions or closed locations. Custom trip summaries: Assistants can tailor summaries based on user needs, from family-friendly attractions spots around a specific hotel. Geo-aware conversational assistants: Travelers can ask questions like “Are there kid-friendly restaurants near my hotel?” and receive factual, grounded answers. 2. Real Estate: Richer Neighborhood Intelligence Real estate platforms can provide far more than square footage and bedroom counts. Personalized property summaries: Summaries adapt based on the buyer’s profile. Families see nearby schools and parks. Young professionals see cafés, and gyms. Targeted neighborhood insights: Apps can guide clients to areas that match their lifestyle, identifying playgrounds for families or coworking spaces for remote workers. More confident decisions: By grounding responses in real neighborhood data, buyers and renters get a deeper understanding of the area before ever visiting. 3. Social Apps & Retail: Hyper-Local, Highly Relevant Suggestions Location-aware experiences become sharper and more personal. Group activity planning: Apps can help friends decide what to do together, from exploring nearby areas to finding a restaurant everyone will enjoy. Hyper-local personalization: Recommendations adapt to preferences and the exact place a user is considering, ideal for food delivery apps, retail discovery, and social platforms.

💡 Watch the demo below to see how you can build a Google Maps Grounding-powered app in just 60 seconds.

⭐⭐⭐

Grounding with Google Maps is one of the most important capabilities for anyone building AI that interacts with the real world. It brings together live geospatial intelligence and powerful reasoning, allowing your application to deliver responses that are not only smart but also factual, fresh, and fully traceable.

Whether you are designing travel experiences, real estate summaries, customer assistants, or location-aware enterprise tools, grounding ensures your AI is accurate and trustworthy.

If you are planning to build AI applications that require real-world context reach out to us today. We can help you design a grounded, production-ready solution with Gemini and Vertex AI.