How to Build a Spatial AI Assistant with the Most Effective Prompts

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:

1. Define the spatial context(coordinates, orientation, points of interest).
2. 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).
3. 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.

IMPORTANT: We are in 2026. All temporal references must be current. Do not mention past years such as 2024, 2023, etc. The content must be fresh and relevant to the present. CURRENT CONTEXT (October 2026): Here are some recent trends and news to inspire you: - Infini-AI-Lab Releases Hermit, an Open-Source Harness Adapter Framework for AI Agents: Infini-AI-Lab released Hermit, an Apache-2.0 harness adapter framework that trains an external layer of instructions, skills, memory and tools from ta... [2026-10-29] - Block Unveils Dessa, a Full-Duplex Speech Model Trained Only on Speech: Block’s AI lab released Dessa, a full-duplex speech-to-speech model trained only on human conversation audio. It runs locally on a MacBook Pro M4 Ma... [2026-10-29] - Prime Intellect Releases Prime Mega MoE: Trainable Fused MoE Kernel, 3.11x Faster Forward Pass: Prime Intellect released Prime Mega MoE, a backward pass for DeepSeek’s fused Mega MoE kernel. It makes the kernel trainable and speeds Qwen3 MoE tr... [2026-10-29] Use this current information as inspiration to create an original and relevant 2026 prompt.