Structured Prompting Strategies for Reducing LLM Hallucination in Enterprise Applications

Abstract

We study how prompt structure affects factual reliability when large language models are deployed in regulated enterprise settings. Across a benchmark of finance, healthcare, and legal tasks, we find that source-anchored, constraint-explicit prompting reduces unsupported claims by a wide margin relative to unstructured prompting—without fine-tuning. We distill the findings into a deployable playbook.

Problem

Enterprises in regulated industries want LLM assistance but cannot tolerate fabricated facts. Fine-tuning is expensive and slow to update; the practical lever most teams actually control is the prompt. Yet prompt design is usually treated as folklore rather than something measured.

Approach

A benchmark of enterprise tasks with verifiable ground truth across three regulated domains
A taxonomy of prompting strategies: source-anchoring, explicit constraints, role specification, and few-shot grounding
Automated hallucination scoring that measures the proportion of claims unsupported by provided sources

Findings

Source-anchored prompting (“based only on the following document…”) produced the largest single reduction in unsupported claims
Combining source-anchoring with explicit refusal instructions further lowered error rates with negligible cost to helpfulness
Gains held across all three domains, suggesting the strategies transfer rather than overfit to one task type

The Playbook

The paper closes with a concrete checklist teams can apply immediately: anchor every factual task to provided sources, make refusal an explicit allowed output, constrain format, and measure hallucination continuously in production. These same practices form the backbone of my enterprise LLM workshops.

Resources

The preprint, benchmark, and evaluation framework are linked above.