Effective Work Samples for Evaluating AI-Powered User Story Generation Skills

AI-powered user story generation represents a critical intersection of product management and artificial intelligence expertise. As organizations increasingly integrate AI capabilities into their products, the ability to craft clear, effective user stories that leverage AI technologies has become an essential skill. These user stories must balance technical feasibility with genuine user needs while accounting for the unique capabilities and limitations of AI systems.

Evaluating a candidate's proficiency in AI-powered user story generation requires more than just reviewing their resume or asking theoretical questions. Practical work samples provide tangible evidence of a candidate's ability to translate complex AI capabilities into user-centric stories that development teams can implement. These exercises reveal how candidates think about AI applications, how they structure requirements, and how they communicate technical concepts to diverse stakeholders.

The following work samples are designed to assess various dimensions of AI-powered user story generation, from technical understanding to prioritization skills. By observing candidates as they work through these exercises, hiring managers can gain valuable insights into how candidates approach the unique challenges of defining AI features through user stories. These samples also evaluate a candidate's ability to iterate based on feedback—a crucial skill when working with emerging technologies like AI.

Implementing these exercises as part of your hiring process will help identify candidates who not only understand AI technologies but can also effectively communicate how these technologies should be implemented to deliver user value. This combination of technical knowledge and communication skill is precisely what organizations need to successfully develop AI-enhanced products that meet real user needs.

Activity #1: AI Feature User Story Creation

This exercise evaluates a candidate's ability to translate a high-level AI capability into well-structured user stories that development teams can implement. It tests their understanding of both user story formatting and AI capabilities, as well as their ability to think through implementation details and acceptance criteria.

Directions for the Company:

• Provide the candidate with a description of an AI capability (e.g., "sentiment analysis of customer feedback") and a brief product context (e.g., "a customer service dashboard").
• Ask them to create 3-5 user stories that leverage this AI capability within the product context.
• Provide a template for user story format if your organization uses a specific one, or allow candidates to use standard formats (As a [user], I want [capability] so that [benefit]).
• Allow 30-45 minutes for this exercise.
• Provide access to a document editor or user story creation tool.

Directions for the Candidate:

• Review the AI capability and product context provided.
• Create 3-5 user stories that implement the AI capability in meaningful ways for the product.
• For each user story:
• Clearly identify the user role
• Describe the capability in specific, implementable terms
• Articulate the benefit to the user
• Include acceptance criteria that would verify successful implementation
• Consider technical feasibility and implementation complexity in your stories.
• Be prepared to explain your thought process and prioritization decisions.

Feedback Mechanism:

• After reviewing the user stories, provide feedback on one strength (e.g., "Your stories effectively captured the business value of sentiment analysis") and one area for improvement (e.g., "The acceptance criteria could be more specific about accuracy thresholds").
• Ask the candidate to revise one of their user stories based on the improvement feedback.
• Observe how they incorporate the feedback and whether they ask clarifying questions before making changes.

Activity #2: AI Feature Prioritization Exercise

This activity assesses a candidate's ability to evaluate and prioritize AI-powered features based on user value, technical feasibility, and business impact. It reveals their strategic thinking and understanding of AI implementation challenges.

Directions for the Company:

• Create a list of 8-10 potential AI-powered features for a product (e.g., "predictive text suggestions," "automated content categorization," "anomaly detection in user behavior").
• For each feature, provide a brief description and some basic information about technical complexity and potential user impact.
• Prepare a simple prioritization framework or matrix for candidates to use.
• Allow 30 minutes for this exercise.
• Provide a spreadsheet or prioritization tool for the candidate to use.

Directions for the Candidate:

• Review the list of potential AI features provided.
• Using the prioritization framework, evaluate each feature based on:
• User value/impact
• Technical feasibility
• Implementation effort
• Strategic alignment with product goals
• Create a prioritized list of the features with brief justifications for your decisions.
• Select the top 3 features and write one user story for each that captures the core functionality.
• Be prepared to explain your prioritization methodology and the rationale behind your decisions.

Feedback Mechanism:

• Provide feedback on the candidate's prioritization approach, highlighting one effective aspect of their methodology and one area where their analysis could be strengthened.
• Ask the candidate to reconsider the priority of one specific feature based on additional information you provide (e.g., "We've learned that this feature would require significant training data that we don't currently have").
• Have them explain how this new information changes their prioritization and user story.

Activity #3: Technical-to-Business Translation

This exercise evaluates a candidate's ability to translate complex AI capabilities into business-friendly language that stakeholders can understand. It tests their communication skills and ability to bridge the gap between technical possibilities and business needs.

Directions for the Company:

• Prepare a technical description of an AI capability written in technical language (e.g., a description of a natural language processing model with technical specifications).
• Create a scenario where the candidate needs to explain this capability to non-technical stakeholders who need to understand its business applications.
• Allow 20-30 minutes for preparation and 10 minutes for presentation.
• If possible, have both technical and non-technical team members present for the presentation.

Directions for the Candidate:

• Review the technical description of the AI capability provided.
• Prepare a brief presentation (5-10 minutes) that explains:
• What the AI capability does in non-technical terms
• How it could benefit users and the business
• What limitations or considerations stakeholders should be aware of
• 2-3 potential use cases illustrated through simple user stories
• Use analogies, visuals, or examples to make complex concepts accessible.
• Avoid unnecessary technical jargon while still being accurate about capabilities.
• Be prepared to answer questions from both technical and non-technical perspectives.

Feedback Mechanism:

• After the presentation, provide feedback on one aspect of the communication that was particularly effective and one area where clarity could be improved.
• Ask the candidate to re-explain one concept based on the feedback, observing how they adjust their communication approach.
• If possible, have a non-technical team member ask a follow-up question to see how the candidate handles clarification.

Activity #4: AI Limitation Problem-Solving

This activity tests a candidate's understanding of AI limitations and their ability to design user stories that account for these constraints. It reveals their problem-solving skills and practical knowledge of AI implementation challenges.

Directions for the Company:

• Create a scenario where an AI feature has limitations that need to be addressed (e.g., a speech recognition system that struggles with certain accents, or an image recognition system with accuracy issues in low light).
• Provide context about user expectations and business requirements for the feature.
• Include any relevant constraints (budget, timeline, available data).
• Allow 45 minutes for this exercise.
• Provide access to a document editor or whiteboard for solution sketching.

Directions for the Candidate:

• Review the AI limitation scenario provided.
• Develop a solution approach that addresses the limitation while still delivering user value.
• Create a set of user stories that implement your solution, including:
• Stories for the core AI functionality
• Stories for handling edge cases and limitations
• Stories for user feedback mechanisms or fallback options
• For each story, include acceptance criteria that address both the happy path and limitation scenarios.
• Sketch a simple user flow diagram showing how users would interact with the feature, including how limitations are handled.
• Be prepared to explain your approach and the tradeoffs you considered.

Feedback Mechanism:

• Provide feedback on the candidate's solution approach, highlighting one strength in their problem-solving and one aspect that could be enhanced.
• Present a new constraint or requirement that challenges their solution (e.g., "We've learned that users are unwilling to provide manual feedback when the AI fails").
• Ask the candidate to revise one or more of their user stories to address this new information, observing how they adapt their approach.

Frequently Asked Questions

How long should each of these exercises take?

Each exercise is designed to take between 30-60 minutes, depending on the complexity and depth you want to explore. For a comprehensive assessment, you might spread these across multiple interview stages rather than conducting all four in a single session.

Should candidates complete these exercises during the interview or as take-home assignments?

Both approaches have merit. Activities #1 and #2 work well as take-home assignments, allowing candidates time for thoughtful analysis. Activities #3 and #4 are often more revealing when conducted live, as they test the candidate's ability to think on their feet and respond to feedback.

How should we evaluate candidates who have strong product skills but limited AI experience?

Focus on their ability to ask good questions about AI capabilities and limitations. Strong product managers can learn technical details, but the ability to structure requirements clearly and think critically about implementation challenges is fundamental. Consider pairing such candidates with an AI expert during the exercise to observe their collaboration skills.

What if our organization uses a different user story format than the candidate?

The specific format is less important than the content and structure of the stories. Look for clarity, completeness, and whether the stories effectively communicate user needs and implementation requirements. You can always provide your preferred format in the instructions if standardization is important.

How can we make these exercises fair for candidates with different backgrounds?

Provide sufficient context about both the product and the AI capabilities involved. Consider offering a brief primer on the AI technology being discussed to level the playing field. Focus evaluation on the candidate's approach and reasoning rather than specific technical knowledge that could be quickly acquired on the job.

Can these exercises be adapted for different levels of seniority?

Yes. For junior candidates, you might simplify the scenarios and focus more on basic user story structure and understanding of AI concepts. For senior candidates, increase complexity by adding organizational constraints, stakeholder management challenges, or strategic considerations to the exercises.

The ability to generate effective AI-powered user stories is becoming increasingly valuable as organizations integrate artificial intelligence into their products. By implementing these practical work samples, you can identify candidates who not only understand AI technologies but can also translate that understanding into clear, implementable user stories that drive product development.

These exercises evaluate multiple dimensions of this specialized skill set—from technical understanding to communication ability to strategic thinking. The feedback mechanisms built into each activity also reveal a candidate's adaptability and receptiveness to input, critical traits when working with rapidly evolving technologies like AI.

For more resources to enhance your hiring process, explore Yardstick's suite of AI-powered tools, including our AI Job Descriptions generator, AI Interview Question Generator, and AI Interview Guide Generator.

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