Analyze Architectural Renders with the Gemini API: A Step-by-Step Guide

What will you learn in this tutorial?

This guide explains the basic workflow for sending an architectural render and an image to the Gemini API, then asking the model to examine it. Rather than having the image automatically approved, the goal is to describe its elements, classify visible features, or ask questions about the render. Google’s documentation says Gemini models can be used for multimodal tasks such as image description, classification, and visual question answering.

In architecture and visualization workflows, this approach can help turn visible scene elements into text, check notable features in a presentation image, or prompt discussion about a render. The model’s response is not a design decision or technical verification. Treat it as an auxiliary assessment based on the image, and compare it with project information.

Requirements

The workflow requires access to the Gemini API, an image, and a clear instruction for examining it. The source documentation includes examples in Python, JavaScript, Java, Go, and REST. The examples show gemini-3.8-flash as the model ID; since no specific SDK version or pricing information is provided, no assumptions are made about them here.

You can provide the image in three ways:

• Public URL: The documentation lists a URL as an option for publicly accessible images.

• Inline Base64: Image data is included in the request alongside the text input. The total request size—including text prompts, system instructions, and image data—must not exceed 20 MB.

• Files API: For larger files or when the same image will be used in multiple requests, upload it and include the returned URI and MIME type in the request.

The example under the source’s URL heading also uses the URI of a file uploaded with the Files API. For this reason, check that the submission method you choose in your implementation matches the current examples in the API documentation. For the inline method, consider the Files API option recommended in the documentation for files approaching the size limit.

Step-by-step architectural render analysis

1. Choose an analysis goal. First, decide what you want to ask about the render. Instead of a general request like “analyze the image,” choose a specific topic, such as visible room elements, the impression of the lighting, or the composition. Don’t expect the model to infer project information that isn’t visible in the image.

2. Submit the image using the appropriate method. If the image is publicly available, consider the URL option. For smaller files, you can use inline Base64 data; with this method, the total request size must stay within the 20 MB limit. If the file is large or you plan to reuse the same image, upload it with the Files API and include its URI and MIME type in the request. With any method, ensure that the text instruction and image input serve the same analysis goal.

3. Write a clear, focused prompt. Google’s prompt design guide recommends making instructions clear and specific, and describing the expected response format when needed. The original example below asks for a list of only the visible elements in an interior render:

Bu iç mekân render’ını incele. Yalnızca görüntüde açıkça seçilebilen mekân öğelerini listele: duvar, döşeme, mobilya, aydınlatma ve açıklıklar. Görselde olmayan bilgileri tahmin etme. Emin olmadığın noktaları “belirsiz” olarak belirt. Yanıtı kısa maddeler hâlinde ver.

This prompt narrows the scope of the analysis and explicitly says not to guess information that isn’t visible. Also compare the response with the image and project documentation.

1. Ask it to classify a visible quality. Defining the options in advance makes the scope of a classification request clear. The example below labels the impression of the lighting; it does not ask for a physical illuminance measurement:

Görseldeki aydınlatma izlenimini şu seçeneklerden biriyle sınıflandır: doğal ışık baskın, yapay ışık baskın, dengeli veya belirsiz. Seçimini destekleyen, görüntüde görülebilen iki ipucu yaz. Ölçüm değeri verme.

This classification can make it easier to create notes under the same headings for different images. However, the response does not mean that the lighting calculation or render settings have been verified.

1. Ask a specific question about the image. In a visual question-answering workflow, it helps to limit the answer to observable elements. For example, you can ask the model to describe the area that stands out in the composition:

Bu mimari görselde izleyicinin dikkatini ilk çekmesi muhtemel alanı tarif et. Görüntüdeki konumunu ve bu yorumu destekleyen görünür kompozisyon öğelerini belirt. Tasarım niyeti hakkında çıkarım yapma.

The model’s interpretation does not measure the actual response of viewers. Don’t present the answer as the result of user research or as a definitive design assessment.

1. Specify the response format and review the result. In your prompt, you can ask for a short paragraph, bullet points, or specific headings. The guide emphasizes that examples can also steer the response format and that prompt design is an iterative process. If the first response is too general, narrow the task—for example, ask it to list only visible materials instead of providing a general opinion.

Common mistakes

• Giving vague instructions: “Evaluate the render” describes a broad task. Specify the feature to examine and the response format.

• Expecting precise technical data from an image: A description or classification is not a measurement report or verification of the project model.

• Asking about information that isn’t visible: Don’t expect a definitive answer about a material brand, dimension, or structural system that doesn’t appear in the image. Ask the model to explicitly identify uncertainty.

• Choosing an image submission method without considering your needs: Inline data has a 20 MB limit for the total request. For large images or images you’ll reuse, consider the Files API option recommended in the documentation.

• Sharing the output without checking it: Compare generated descriptions with the image, and don’t copy unsupported interpretations directly into presentations or project notes.

Impact on architecture and visualization workflows

This method can help architecture firms, interior designers, and visualization teams describe renders and organize initial review notes. Teams that want to reuse the same criteria can standardize their prompts. The choice between sending an image by URL, Base64, or the Files API depends on accessibility, file size, and whether the image needs to be reused.

API use does not replace existing rendering software; the sources provide no information about hardware requirements or compatibility with visualization software. Before adopting it, teams should separately assess API access and any costs not specified in the sources. Model responses should not be the sole basis for design or technical decisions; they should be checked against the image and project data.

Next steps

Start with a single render and one focused question. Then try different but clearly worded prompts with the same image to see how the responses vary. When comparing multiple images, using the same criteria and response format can make the results easier to organize; still, limit the comparison to information visible in the images.

For more specialized tasks, such as locating objects in an image or segmenting regions, the documentation says accuracy can be improved through additional training for specific use cases. This does not mean the same level of performance will be achieved with every architectural image. If your needs go beyond basic description or question answering, separately validate the method and its output.

Sources and license

This tutorial has been adapted into Turkish from Google’s Gemini API image understanding and prompt design documentation. Both sources are licensed under CC BY 4.0. The architectural render prompts here were written originally and were not copied from the source examples.