Common Prompt Patterns

The question-answer format is the most straightforward prompt pattern, ideal for gathering specific information or clarifying concepts. This pattern works best when you need direct, factual responses about features, user behaviors, or market trends.

Structure your questions to be specific and include relevant context to avoid ambiguous answers. Effective questions focus on one topic at a time and specify the desired depth of response. For instance, instead of asking "What about user onboarding?", ask "What are the 3 most common friction points in mobile app onboarding flows?" This precision helps AI provide targeted, actionable insights.

You can use this pattern during research phases, competitive analysis, or when validating assumptions. The key is balancing specificity with openness to discover unexpected insights that might inform product decisions.

Task completion prompts direct AI to perform specific actions like drafting emails, creating outlines, or generating documentation. These prompts should clearly define the task, expected format, and any constraints or requirements. Start with action verbs like "create," "draft," "generate," or "compile" to signal the desired outcome.

Include all necessary context upfront: target audience, tone, length requirements, and key points to cover. For example: "Draft a 200-word product update email for enterprise customers highlighting our new features, maintaining a professional yet approachable tone." This clarity ensures outputs align with your needs.

Task completion prompts excel at accelerating routine work while maintaining consistency across team outputs. They're particularly valuable for creating first drafts, standardizing communications, and generating variations of content for different audiences or channels.

Analysis and evaluation prompts help product teams examine data, feedback, or scenarios to extract meaningful insights. Structure these prompts by presenting the information to analyze, followed by specific evaluation criteria or questions. This pattern transforms raw data into actionable intelligence for product decisions.

Effective analysis prompts include clear parameters: what to analyze, which aspects to focus on, and what conclusions to draw. For instance: "Analyze these 5 customer reviews and identify common pain points related to checkout flow, then suggest 3 potential solutions." This approach ensures focused, relevant analysis.

These prompts are invaluable for processing user feedback, evaluating feature proposals, or assessing market opportunities. They help teams move beyond surface-level observations to uncover patterns and insights that drive product improvements.

Creative generation patterns work best when you provide clear creative constraints while leaving room for unexpected outputs. Include parameters like quantity ("generate 10 ideas"), theme, target audience, and any specific requirements or limitations.

Structure creative prompts to balance guidance with freedom. For example: "Generate 5 innovative features for a fitness app targeting busy parents, focusing on 10-minute-or-less activities." This gives direction while encouraging creative exploration within defined boundaries.

Product teams can use these patterns for brainstorming sessions, feature ideation, and exploring new market opportunities. The key is treating AI outputs as starting points for further refinement rather than final solutions.

List and structure requests organize information into digestible formats like bullet points, tables, or hierarchies. These prompts excel at transforming complex information into scannable, actionable formats. Specify the desired structure explicitly: "Create a prioritized list," "Organize into categories," or "Present as a comparison table."

Include formatting preferences and organization criteria in your prompt. For example: "List the top 10 user onboarding best practices, organized by implementation difficulty (easy, medium, hard) with brief explanations for each." This ensures outputs match your team's needs for quick reference and decision-making.

You can use these patterns for creating checklists, organizing research findings, or structuring requirements. Well-formatted information accelerates comprehension and facilitates better team alignment on priorities and next steps.

Comparison frameworks help evaluate multiple options, features, or solutions against consistent criteria. Structure these prompts by defining what to compare, which attributes matter, and how to present findings. This pattern transforms subjective decisions into objective evaluations based on clear parameters.

Effective comparison prompts specify evaluation criteria upfront. For instance: "Compare these 3 analytics tools based on pricing, integration capabilities, learning curve, and customer support quality. Present findings in a pros/cons table." This structured approach ensures comprehensive, unbiased comparisons.

Step-by-step instruction prompts generate clear, sequential guidance for completing tasks or processes. These prompts should specify the starting point, desired outcome, and any constraints or considerations. Include the target audience's skill level to ensure appropriate detail and terminology.

Structure these prompts with clear parameters: "Create step-by-step instructions for [task] suitable for [audience], including [specific elements]." For example: "Write beginner-friendly instructions for setting up a new account on our app, including screenshots suggestions and common troubleshooting tips."

Product teams can use these patterns for creating user guides, internal documentation, and onboarding materials. Well-crafted instructions reduce support tickets and accelerate user adoption by providing clear paths to success.

Template-based prompts use structured formats to ensure consistent, comprehensive outputs across similar tasks. Create reusable frameworks with placeholder variables that team members can customize. This pattern standardizes quality while maintaining flexibility for specific needs.

Design templates that capture essential elements while allowing customization. For example: "Using this template: [User Story: As a {user type}, I want to {action} so that {benefit}], create 5 user stories for {specific feature}." This approach ensures all stories follow best practices while addressing unique requirements.

Product teams can leverage templates for tasks like writing user stories, feature specifications, and research summaries. Once established, templates accelerate work while ensuring nothing important gets overlooked. They're particularly valuable for distributed teams needing consistent documentation standards.

Constraint-based patterns deliberately limit parameters to focus creative output and ensure practical solutions. These prompts specify boundaries like budget, timeline, technical limitations, or resource availability. Constraints paradoxically enhance creativity by forcing innovative thinking within realistic parameters.

Frame constraints as creative challenges rather than limitations. For example: "Design 3 feature concepts for improving user engagement that require no additional engineering resources and can launch within two weeks." This approach generates immediately actionable ideas rather than unrealistic concepts.

Constraint-based patterns are especially valuable during planning phases when balancing ambition with available resources. Clear constraints lead to more innovative, implementable solutions.