Mapping with In-Memory Layers to Reduce LLM Overload
5 hours ago
- #Data Pipeline
- #LLM Orchestration
- #Map Generation
- RidgeText uses an LLM for natural language understanding and tool orchestration via SMS, avoiding traditional UIs.
- A layer-first pattern prevents passing large GeoJSON through the LLM; instead, data is queued server-side with lightweight acknowledgments.
- Map generation involves independent layer retrieval (e.g., wildfires, trails) and compositing by a deterministic render step, reducing token usage significantly.
- This approach mirrors Mapbox's source-layer model, allowing flexibility in renderers (e.g., static tiles or headless GL) without changing LLM interfaces.
- Trade-offs include the LLM's inability to reason about underlying geometry and ephemeral layer queues, though persistence could address the latter.
- The pattern applies beyond maps to scenarios like multi-source data enrichment and log analysis, removing the LLM as a data pipe.