How to Think Before You Prompt

Emily Yang

Human-Centred AI (HCAI) Expert

Most poor AI outputs begin before the prompt is written. Learn how clarity on purpose, audience, constraints, and format transforms results.

Data Science

Most poor AI outputs begin before the prompt is written. Learn how clarity on purpose, audience, constraints, and format transforms results.

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How to Think Before You Prompt

2 mins 44 secs

Key learning objectives:

Explain why vague prompts fail
Understand how to improve prompts through clearer intent
Learn how to create more decision-ready outputs

Overview:

Poor AI output is often a symptom of unclear thinking rather than weak technology. This module shows how purpose, audience, constraints, and structure create outputs that are sharper, more relevant, and more useful.

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Summary

Why do so many AI outputs disappoint?
Many people approach AI as if the prompt is the work. They type a quick request, receive something bland, and conclude the tool is limited. In reality, the prompt often reflects an undefined task. If the user has not decided what the work is meant to achieve, who it is for, or what quality looks like, AI has little choice but to generate something generic.

Why does AI default to vague output?
AI predicts plausible language. When context is thin, it fills missing detail with safe, broad wording. That is why many weak outputs sound polished while saying very little.

What creates stronger outputs?
Purpose defines the job to be done. Audience shapes relevance. Constraints narrow the field so the output becomes practical. Structure turns text into something people can scan, decide from, or act on.

What is the real capability being built?
Strong AI users learn to think through the task before asking. Prompting becomes an extension of judgement rather than guesswork.

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Emily Yang

Emily Yang leads Human-Centred AI and Innovation at a global financial institution and serves on the organisation’s AI Safety and Governance committees. Her work focuses on advancing responsible and trustworthy AI systems that balance innovation with accountability. She is among the first practitioners in the industry to apply Human-Centred AI at scale. With over a decade of experience in human-computer interaction and user experience, Emily has held roles across tech startups, corporate venture builders, and major technology companies. Her journey into AI began with studies in biochemistry and neuroscience, followed by a research master’s in HCI and natural language technologies, during which she published work on perceived empathy and emotional intelligence in virtual agents.

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