What is spatial computing and an AI assistant?
Spatial computing extends computer capabilities beyond the 2D screen, incorporating depth, orientation, and geographic context. AI assistants, powered by cutting-edge LLMs like the latest high-speed MoE models, can understand and generate responses that account for this 3D space. Together, they create more natural and contextual experiences, from enhancing augmented reality to navigating dynamic environments.
Why combine spatiality and artificial intelligence?
An assistant that knows its location can:
- Provide real-time, context-aware directions (e.g., "Look left, there's a bus stop").
- Overcome the limitations of generic voice commands with precise spatial references (e.g., "Open the document on the desk").
- Leverage AI-optimized hardware on mobile and edge devices, such as Intel's new MoE chip, for faster processing.
Recent releases from Prime Intellect, Baseten, and Block demonstrate how smaller, specialized models (e.g., Baseten's 2B decision model, Block's full-duplex Dessa voice model) can be integrated into spatial assistants without compromising performance.
How to build a spatial AI assistant with effective prompts
Creating an effective prompt for a spatial assistant involves three steps:
- Define the spatial context(coordinates, orientation, points of interest).
- Select the appropriate AI modelfor the use case (high-speed MoE for complex calculations, lightweight decision model for rapid classification, voice model for direct interaction).
- Formulate the promptthat guides the model to incorporate spatial data into its responses.
Practical example: a prompt for augmented reality navigation
Suppose we want to ask, "Where can I find a nearby coffee shop?" and the device knows our location (latitude 45.4642, longitude 9.1900) and orientation (heading 30° clockwise from north). The following prompt sends this information to a MoE-compatible LLM, leveraging the processing speed of Prime Mega MoE:
system: You are a spatial AI assistant for AR devices. Use the user's location (lat: 45.4642, lon: 9.1900) and orientation (heading: 30°) to provide contextual suggestions.
user: Find a nearby coffee shop.
assistant:This prompt makes it clear to the model that the response must be specific to location and orientation, improving relevance and reducing response time.
2026 tools and frameworks for developing spatial assistants
- Prime Intellect's MoE SDK
- Baseten's joey-1
- Block's Dessa
These tools are available via API and can be integrated into AR/VR engines, mobile apps, and edge devices.
Tips for creating effective prompts for spatial contexts
- Include coordinates and units(e.g., meters, degrees) to eliminate ambiguity.
- Specify the required confidence level: "Provide the answer with a confidence ≥ 0.9".
- Use contextual markerslike [VOICE], [AR], [MAP] to indicate the expected channel.
- Test with lightweight models(e.g., joey-1) to ensure scalability before moving to larger models.
Future perspectives and emerging trends
Spatial assistants will become increasingly collaborative, with MoE models, full-duplex voice models, and lightweight reasoning systems working in parallel to provide faster and richer contextual responses. We also expect tighter integration with wearable device and Internet of Things ecosystems, with prompts designed to leverage new sensory modalities (e.g., gestures, eye-tracking).
Conclusion
By combining spatial computing with AI assistants, we can create truly contextual experiences that understand where we are and how we are oriented. Using this year's cutting-edge MoE models, full-duplex voice models, and prompt engineering toolkits, you can design smarter and more responsive assistants. Start with a single use case, integrate location data into your prompts, and scale as your users adopt the technology.
Next steps:
- Define a specific spatial use case (e.g., AR navigation) and identify the necessary input data.
- Experiment with a prompt that includes coordinates, units, and contextual markers.
- Integrate a lightweight model like joey-1 for quick feedback, then scale with an MoE model for more complex calculations.