Research
In PreparationMar 10, 2026In Preparation
Grounding Patterns for Portfolio-Scale LLM Systems
Draft work on constrained prompting and structured grounding to reduce hallucination risk.
Abstract
This work studies practical grounding patterns for small-scope LLM assistants using local structured data and explicit refusal rules.
Contribution
Problem framing, prompt architecture, and evaluation setup for portfolio-assistant reliability.
Status: In preparation.
This draft explores a practical question:
How can we create assistant experiences that remain useful while refusing unsupported claims?
Current focus:
- Structured data serialization quality
- Prompt constraint design
- Source-linked response formatting
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