Step-back prompting is a technique that enhances AI performance by encouraging the model to first explore broader, foundational concepts before addressing specific tasks. Instead of diving directly into a particular problem, the AI first considers general principles, background knowledge, and underlying patterns that can inform a more thoughtful and accurate response.
Here’s a simple step-back prompting example for content creation:
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---provider: OpenAImodel: gpt-4otemperature: 0.6---<step># Step Back: General PrinciplesBefore creating content, let's establish foundational principles.What are the key elements that make {{ content_type }} effective and engaging for {{ target_audience }}?Consider:- Core principles of effective communication- Audience engagement strategies- Industry best practices- Psychological factors that drive engagement## Foundational Elements:</step><step># Apply: Specific Content CreationUsing the principles identified above as guidance:**Context**: {{ foundational_elements }}Now create {{ specific_content_request }} that incorporates these proven principles.## Content:</step>
---provider: OpenAImodel: gpt-4otemperature: 0.7---<step as="creative_principles"># Step Back: Creative PrinciplesWhat are the fundamental elements that make {{ creative_medium }} compelling and memorable?Consider:- Narrative structure principles- Emotional engagement techniques- Audience psychology- Genre conventions and innovations## Creative Elements:</step><step># Apply: Specific CreationUsing these creative principles as foundation:**Elements**: {{ creative_principles }}Create {{ specific_creative_task }} that incorporates these proven elements.**Theme**: {{ creative_theme }}**Audience**: {{ target_audience }}## Creative Work:</step>
Generate multiple perspective frameworks before application:
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---provider: OpenAImodel: gpt-4otemperature: 0.6---<step># Step Back: Multiple FrameworksWhat are three different theoretical approaches to {{ problem_domain }}?For each approach, explain:- Core principles- Key methodologies- Typical applications- Strengths and limitations## Framework 1 - {{ approach_1 }}:## Framework 2 - {{ approach_2 }}:## Framework 3 - {{ approach_3 }}:</step><step># Apply: Integrated SolutionDrawing insights from all three frameworks:**Approaches**: {{ framework_comparison }}Develop a solution for {{ specific_challenge }} that integrates the best elements from each approach.## Integrated Solution:</step>
Use multiple levels of abstraction for complex problems:
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---provider: OpenAImodel: gpt-4otemperature: 0.5---<step># Step Back Level 1: Universal PrinciplesWhat are the most fundamental principles that apply to {{ broad_domain }}?## Universal Principles:</step><step># Step Back Level 2: Domain-SpecificGiven these universal principles: {{ universal_principles }}What specific principles apply to {{ specific_domain }}?## Domain Principles:</step><step># Step Back Level 3: ContextualConsidering both universal and domain principles:**Universal**: {{ universal_principles }}**Domain**: {{ domain_principles }}What contextual factors are unique to {{ specific_context }}?## Contextual Factors:</step><step># Apply: Comprehensive SolutionIntegrating all levels of insight:**Foundation**: {{ all_principles }}Address this specific challenge: {{ precise_problem }}## Solution:</step>
Step back: “How do different experts approach [domain]?”
Apply: “Combine these approaches for [specific challenge]”
Step-back prompting transforms AI responses from reactive to reflective, ensuring that specific solutions are grounded in broader understanding and proven principles.