Introduction: Why Spatial Computing and AI Assistants Are the Perfect Match
To make your next product truly immersive, you need to learn how to write prompts that leverage coordinates, sensors, and multimodal signals. This practical guide walks you through the process step by step, using the latest tools released this year.
Foundations of Spatial Computing and Contextual Interaction
Spatial computing is built on three pillars: position detection, space mapping, and contextual rendering. For an AI assistant, these pillars provide thewhat,where, andwhenneeded to generate relevant responses.
Defining Space with Sensors and APIs
Most modern platforms offer SDKs that expose real-time coordinates, environmental meshes, and points of interest. For example, you can use the@spatial/corelocation API to retrieve the user’s 3D position.
<script>
import { LocationProvider } from '@spatial/core';
const pos = await LocationProvider.getCurrentPosition();
console.log('Coordinates:', pos.x, pos.y, pos.z);
</script>This raw data becomes meaningful when incorporated into prompts for language models. A multimodal prompt might look like this:
"The user is at coordinates (x=2.5, y=1.8, z=-3.2) inside an office with a window oriented north. Describe the scene and suggest a useful action in this spatial context."Designing Multimodal Prompts for Spatial Contexts
When designing a prompt for an LLM in a spatial context, consider these variables:
- User location and orientation
- Surrounding environmental elements (furniture, screens, objects)
- Interface mode (first-person view vs. overlay)
- Available sensory devices (camera, microphone, touch sensors)
An effective model:
"[INSTRUCTION] Use the camera to identify the object in front of you. Return the object's name, its approximate distance (in meters), and a contextual suggestion in one sentence."Modern Tools for Scalable Deployment
Building a spatial assistant isn’t just about prompts; you need infrastructure that handles domains, training pipelines, and edge inference. Three tools released in 2026 simplify this process.
Domain Management with OpenCoreDev Domain SDK 0.2.0
OpenCoreDev just released Domain SDK 0.2.0, a unified TypeScript client for managing custom domains across Vercel, Cloudflare for SaaS, Railway, and other platforms. For a spatial app, this means automatically linking a subdomain that reflects the user’s location (e.g.,assistant-42.example.com) without manual configuration.
import { DomainManager } from '@opencore/domain-sdk';
await DomainManager.add('my-spatial-assistant', {
platform: 'vercel',
region: 'eu-central',
verify: true
});The SDK also verifies DNS, reducing time-to-market for new environments.
Reproducible Pipelines with Gin Config for Spatial Models
If you’re training a model for spatial inference, Gin Config lets you keep the static code and vary experiments via.ginfiles. This is especially useful when testing different 3D assets or sensor configurations.
# config.gin
model {
hidden_size = 1024
num_layers = 8
}
training {
learning_rate = 0.0005
batch_size = 32
}Run the pipeline with:
python train.py --config config.ginEdge Inference with PrismML’s Bonsai 27B
PrismML just released Bonsai 27B, a reduced-bit (1-bit and ternary) version of Qwen3.6-27B that runs on laptops and phones. For a spatial assistant that needs to operate offline, Bonsai offers the same language capability with drastically lower power consumption.
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained('PrismML/Bonsai-27B-int1', device_map='auto')
tokenizer = AutoTokenizer.from_pretrained('PrismML/Bonsai-27B-int1')
prompt = "Describe the room I'm in (coordinates: 0,0,0)."
inputs = tokenizer(prompt, return_tensors='pt').to(model.device)
outputs = model.generate(**inputs, max_length=150)
print(tokenizer.decode(outputs[0]))Practical Example: Building a Spatial AI Assistant
Let’s put it all together. Here’s a step-by-step workflow for an assistant that can describe the surrounding environment and suggest actions.
- Step 1:Use the location API to capture the user’s position.
- Step 2:Use the built-in camera (or an external vision client) to extract objects and scenes via a multimodal model.
- Step 3:Compose a prompt that includes both coordinates and visual data.
- Step 4:Send the prompt to the locally hosted Bonsai 27B model for low-latency inference.
- Step 5:Register the assistant’s domain via Domain SDK for platform-specific secure access.
- Step 6:Display the response as an AR overlay that remains anchored to the detected coordinates.
This workflow keeps each component independent, allowing you to replace, for example, the model with a larger LLM when needed.
Common Mistakes and How to Avoid Them
Even experienced teams make some recurring mistakes when combining spatial computing and AI.
- Ignoring coordinate alignment.Ensure all services (camera, location, rendering) use the same coordinate system; otherwise, prompts will be misleading.
- Writing prompts that are too generic.Contextual prompts work best when they are domain-specific. Always include explicit spatial context.
- Overloading infrastructure.Use configuration files like Gin to avoid code duplication and use reduced-bit models like Bonsai when edge inference is a priority.
- Neglecting domain management.A consistent subdomain simplifies deployment and improves SEO for location-based services.
Conclusion: The Future of Contextual Assistants in Space
In 2026, the convergence of smarter LLMs, more precise spatial sensors, and tools like Domain SDK, Gin Config, and Bonsai 27B opens up new possibilities for truly contextual experiences. Whether you’re building an AR assistant for industrial maintenance, a VR tutor for language learning, or a navigation system for robots, the right components combined with well-designed prompts make all the difference.
Invest in your prompts, keep your assets modular, and leverage modern APIs to create AI assistants that not only respond, but *understand* the space you’re operating in.
Key Takeaways
- Always write multimodal prompts that include coordinates, visual inputs, and user intentions.
- Use Domain SDK 0.2.0 to automatically link custom domains for each spatial instance.
- Adopt Gin Config for reproducible training pipelines when exploring different 3D assets.
- Implement Bonsai 27B for energy-efficient edge inference on spatial devices.
- Test coordinate alignment end-to-end to avoid invisible shifts in AR/VR experiences.