Prompt Chaining for Better AI Research Outputs (Full Transcript)

Learn how prompt chaining structures AI requests into steps to improve the quality of research deliverables like discussion guides.

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[00:00:00] Speaker 1: The bottom line is this. In the context of qualitative and quantitative research tasks, basic prompting often leads to uneven or disappointing results. So let's look at two options both known, structured, prompt engineering approaches, that can elevate the quality of what you get back from AI, and perhaps even save you time. First up, prompt chaining. Sticking with our dental insurance example, instead of asking for the whole guide in one go, with prompt chaining we build it step by step. Step 1. Ask the AI. Outline six logical sections for a 45-minute discussion guide about dental insurance awareness among employed adults without coverage, using section titles and time suggestions. The first section should be a warm-up section.

Summary

Basic prompting can produce uneven results for qualitative and quantitative research tasks. Two structured prompt-engineering approaches can improve output quality and save time. The first is prompt chaining: building an output step by step rather than requesting everything at once. Example given: for a dental insurance discussion guide, first ask the AI to outline six logical sections for a 45-minute guide with section titles and time allocations, starting with a warm-up.

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Title

Using Prompt Chaining to Improve Research Prompting

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Keywords

prompt engineering Remove

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prompt chaining Remove

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qualitative research Remove

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quantitative research Remove

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discussion guide Remove

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dental insurance Remove

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AI prompting Remove

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structured prompts Remove

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Key Takeaways

Basic prompts may yield inconsistent results in research contexts.
Structured prompt-engineering methods can improve quality and efficiency.
Prompt chaining breaks complex outputs into smaller, sequential steps.
Start by requesting a clear outline with sections and time estimates before generating full content.

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Sentiments

Neutral: Informative, instructional tone focused on improving AI output quality; no strong emotional language.

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