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Many market research, UX, and CX teams now use AI for brainstorming, writing, and various research tasks. But it's not always easy, especially when using AI to help researchers craft surveys and discussion guides. The recurring issue isn't effort—it's uneven quality from ad hoc prompting.
In "Prompt Engineering for Researchers," host Kathryn Korostoff demonstrates two structured approaches that keep rigor intact while reducing rework. First, Prompt Chaining: design the deliverable step by step—structure before content—using short review loops to tune timing, probes, and flow.
Second, Reflexion (with an "X"): asking the AI to critique its own draft for bias, confusion, or sequencing and to document changes. Example: "Review this guide and revise any questions that may be leading, biased, or confusing. Then list the changes you made and why you made them." And it will!
Check out this episode for examples of using effective AI prompting for qualitative and quantitative researchers.
#MarketResearch #QualitativeResearch #UXResearch #CXResearch #SurveyDesign #AIforResearch
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