How to Use an AI Assistant in Spatial Computing for Engaging Interactive Experiences

Introduction: Let’s answer the main question straight away

If you’re wonderinghow to use an AI assistant in spatial computing, the short answer is:with structured prompts that guide the model to interpret space, objects, and interactions in real time.By 2026, the integration between conversational AI and spatial technologies (augmented reality, virtual reality, 3D mapping, and robotics) has reached a level of maturity that allows anyone to create immersive experiences without having to write complex code.

The fundamentals of spatial computing and AI in 2026

What makes spatial computing unique today

Spatial computing is no longer limited to overlaying 3D graphics onto the real world. Three trends dominate the current landscape:

• Immersive sensors: high-fidelity depth, eye-tracking, and hand-tracking are now standard on commercial AR/VR devices.
• Integration with language models: LLMs are capable of understanding coordinates, spatial layers, and contextual semantics.
• Edge computing: devices process data locally, reducing latency to a few milliseconds.

This convergence paves the way for AI assistants that cansee,hear, andactwithin dynamic environments.

The new foundation models for physical AI (GEN-1.5, XPENG IRON)

2026 saw the release ofGEN-1.5, a robot foundation model that learns new physical tasks from a single 3-12 second demonstration. Meanwhile, XPENG IRON has raised over $900 million to scale its humanoid robot platform. These models are already accessible via APIs that can be combined with spatial AI assistants to:

• Generate realistic robotic movements based on textual descriptions.
• Interpret natural language to plan actions in a 3D space.
• Adapt to new environments through continuous learning based on demonstrations.

Designing effective prompts for spatial AI assistants

The structure of a spatial prompt

A prompt for a spatial AI assistant should contain four key components:

1. Spatial context: coordinates, dimensions, layers.
2. User goal: desired action (e.g., "place a 3D model on the table")
3. Constraints: materials, time, aesthetic preferences.
4. Desired output: format (mesh, animation, robot instruction).

Example prompt in Italian:

You are an AI assistant specialised in spatial computing. I need to place a 3D model of a plant on the table top visible from the webcam, so that it is oriented towards the window and has realistic lighting. Provide the exact commands for the rendering engine and a short Python script that uses OpenXR and Mediapipe for contour detection.

This prompt provides the assistant with a clearframeworkfor generating both rendering instructions and usable code.

Practical use cases

Here are some real-world scenarios you can replicate today:

• AI-guided navigation: an AR tutor indicates routes within a warehouse, overlaying location-based arrows and safety warnings.
• Collaborative 3D modelling: two designers use the same shared virtual space; the AI assistant suggests geometry or texture modifications based on natural language.
• Training humanoid robots: an operator demonstrates a simple movement to GEN-1.5 via a 5-second demo; the AI assistant generates a sequence of commands for XPENG IRON.
• Educational augmented reality: a museum uses an AI assistant to describe historical artefacts, overlaying contextual information directly on the physical object.

Each of these cases relies on a well-structured prompt and integration with cutting-edge physical AI models.

Best practices and quick tips

• Use explicit spatial language: terms like "centre", "corner", "layer" reduce ambiguity.
• Define units of measurement: metric or imperial? Maintain consistency throughout the prompt.
• Incorporate visual references: describe textures, colours, and lighting to guide the model toward more realistic outputs.
• Test with an MVP: generate a prototype, verify the output, and iterate on the prompt.
• Leverage edge computing APIs: many providers now offer local inference for LLMs optimised for spatial computing.

Upcoming trends to watch

Exciting developments await us in 2026.ME-POIs, Google Research's framework that integrates actual use of a place into textual embeddings, is becoming a standard for assisted navigation. Additionally, foundation models for robots like GEN-1.5 are paving the way for a new generation of physical AI assistants that can learn directly from the surrounding world.

Conclusion: 3 concrete actions to start right now

By following these steps, you’ll transform the AI assistant into a powerful tool for spatial computing, creating experiences that are both intuitive, visual, and highly engaging.