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AI Prompt Architect
Labor Efficiencype-citation-113P1

Instruction-tuned models follow natural language prompts far more reliably.

FLAN-T5 improved average performance…FLAN-T5 improved average performance across 1,836 tasks by 9.4% over the base model, with instruction tuning enabling zero-shot generalisation to entirely unseen task types.
Chung et al., 'Scaling Instruction-Finetuned Language Models (FLAN-2)', Google Research, 2022

Context & Methodology

Instruction tuning teaches models to follow prompt structures, making them dramatically more responsive to well-crafted STCO-style prompts.

Applies To

google

Confidence Level

High

Implementation Effort

low

Recommendation

follow

Execution Priority

P1

Put This Evidence to Work

Use the STCO framework to implement findings like this in structured, testable prompts.

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