Essential Work Samples for Evaluating AI System Design Skills

Designing effective AI systems requires a deep understanding of their inherent limitations. As AI technologies become increasingly integrated into business operations, the ability to design solutions that work within these constraints is a critical skill. AI systems, despite their power, have specific boundaries in terms of context handling, knowledge cutoffs, reasoning capabilities, and more. Professionals who can navigate these limitations while maximizing AI capabilities are invaluable to organizations implementing these technologies.

The challenge in hiring for AI design roles lies in evaluating a candidate's practical understanding of these limitations. While theoretical knowledge is important, the ability to apply this understanding to real-world scenarios is what truly differentiates exceptional candidates. Through carefully crafted work samples, employers can assess how candidates approach AI system design with limitations in mind, how they implement safeguards, and how they communicate these constraints to stakeholders.

These work samples provide a window into a candidate's problem-solving approach, technical knowledge, and strategic thinking when it comes to AI system design. They reveal whether candidates can anticipate potential issues before they arise and design systems that gracefully handle edge cases. Moreover, they demonstrate a candidate's ability to balance technical constraints with business requirements—a crucial skill in practical AI implementation.

The following exercises are designed to evaluate a candidate's proficiency in designing for AI system limitations across different dimensions: system architecture planning, prompt engineering, stakeholder communication, and error handling. By using these work samples, hiring managers can gain valuable insights into how candidates would approach real challenges in AI system design and implementation.

Activity #1: AI System Architecture Planning

This activity evaluates a candidate's ability to design an AI system architecture that accounts for known limitations while meeting business requirements. It tests their understanding of AI model constraints, their ability to plan for graceful degradation, and their skill in creating systems that balance technical limitations with user needs.

Directions for the Company:

• Provide the candidate with a written brief describing a business problem that requires an AI solution, including specific requirements and constraints.
• The brief should include details about user needs, expected volume of requests, required response times, and any compliance considerations.
• Example scenario: "Design an AI-powered customer service system that can handle 10,000+ inquiries daily, must respond within 3 seconds, needs to maintain context across multiple user interactions, and must comply with financial services regulations."
• Allow candidates 45-60 minutes to complete their design.
• Provide access to paper/whiteboard or digital diagramming tools.

Directions for the Candidate:

• Review the business requirements and constraints provided.
• Create a system architecture diagram showing how your AI solution would work, including all major components.
• Explicitly identify at least 5 potential AI limitations that could affect this system and how your design addresses each one.
• Include fallback mechanisms for when AI components fail or reach their limitations.
• Prepare a brief explanation of your design choices and tradeoffs (5-10 minutes).

Feedback Mechanism:

• After the candidate presents their design, provide feedback on one strength of their approach (e.g., "Your consideration of token limitations was thorough and practical").
• Provide one area for improvement (e.g., "Your design didn't fully address how to handle the AI's knowledge cutoff date").
• Give the candidate 10 minutes to revise their approach based on this feedback.

Activity #2: Constrained Prompt Engineering

This activity tests a candidate's ability to work within the token and context limitations of large language models. It evaluates their prompt engineering skills and their understanding of how to maximize AI performance despite inherent constraints.

Directions for the Company:

• Prepare a complex task that would typically require extensive context or instructions (e.g., summarizing a long document, creating a specific type of analysis).
• Create a deliberately constrained scenario: "You must complete this task using a model with a maximum of 1,000 tokens for both input and output combined."
• Provide the necessary materials (e.g., a long document to be summarized, complex data to be analyzed).
• Allow 30 minutes for the exercise.

Directions for the Candidate:

• Review the task requirements and materials provided.
• Design a prompt strategy that breaks down the complex task into manageable chunks that fit within the token constraints.
• Write out the exact prompts you would use, showing how you would sequence them to complete the full task.
• Explain your approach to chunking information, maintaining context across prompts, and ensuring consistency in the final output.
• Identify potential failure points in your approach and how you would address them.

Feedback Mechanism:

• Provide feedback on the effectiveness of their prompt design (e.g., "Your chunking strategy was efficient and maintained context well").
• Suggest one improvement (e.g., "Your prompts could be more specific about maintaining a consistent tone across chunks").
• Allow the candidate 5-10 minutes to revise one of their prompts based on this feedback.

Activity #3: AI Limitations Stakeholder Role Play

This role play assesses a candidate's ability to communicate AI system limitations to non-technical stakeholders effectively. It evaluates their understanding of AI constraints and their skill in managing expectations while maintaining stakeholder confidence.

Directions for the Company:

• Prepare a scenario where an ambitious stakeholder has unrealistic expectations about AI capabilities.
• Example: "You're meeting with a marketing executive who wants to build an AI system that can perfectly predict customer behavior, generate flawless creative content, and operate without any human oversight."
• Assign someone to play the role of the enthusiastic but misinformed stakeholder.
• Provide the role player with specific misconceptions to express and questions to ask.
• Allow 15-20 minutes for the role play.

Directions for the Candidate:

• Listen carefully to the stakeholder's requirements and expectations.
• Identify the unrealistic expectations that exceed current AI capabilities.
• Explain AI limitations clearly without being overly technical or dismissive.
• Propose alternative approaches that work within AI constraints while still addressing the stakeholder's core business needs.
• Be prepared to answer questions about why certain limitations exist and when they might be overcome.

Feedback Mechanism:

• Provide feedback on how effectively the candidate communicated complex limitations (e.g., "You explained hallucination risks in a way that was accessible and practical").
• Suggest one improvement (e.g., "You could have offered more concrete examples of how these limitations might affect their specific use case").
• Continue the role play for 5 more minutes, allowing the candidate to incorporate the feedback.

Activity #4: AI Error Detection and Mitigation

This technical evaluation assesses a candidate's ability to identify AI system errors resulting from inherent limitations and implement effective mitigation strategies. It tests their practical understanding of how AI systems fail and how to design robust solutions.

Directions for the Company:

• Prepare a dataset of AI system outputs that contain various types of errors resulting from different limitations (hallucinations, reasoning errors, context misunderstandings, etc.).
• Include 10-15 examples with varying degrees of subtlety.
• Provide a description of the intended AI system and its purpose.
• Allow 45 minutes for the exercise.

Directions for the Candidate:

• Review each AI output and identify the type of error or limitation that caused it.
• Categorize the errors based on their root causes (e.g., token limitation, reasoning failure, etc.).
• For each category of error, design a detection mechanism that could identify similar errors automatically.
• Propose mitigation strategies for each error type, including both technical solutions and process changes.
• Create a prioritized list of which limitations pose the greatest risk to system reliability and why.

Feedback Mechanism:

• Provide feedback on the comprehensiveness of their error detection approach (e.g., "You identified subtle reasoning errors that many would miss").
• Suggest one improvement to their mitigation strategy (e.g., "Your approach to handling hallucinations could be more robust by incorporating verification steps").
• Give the candidate 10 minutes to revise their highest-priority mitigation strategy based on this feedback.

Frequently Asked Questions

How long should we allocate for these work samples?

Each activity is designed to take between 30-60 minutes. For a comprehensive assessment, you might want to select 1-2 activities rather than conducting all four. Choose the ones most relevant to your specific role requirements.

Should we provide these exercises as take-home assignments or conduct them during interviews?

The AI System Architecture Planning and Error Detection activities work well as take-home assignments with a time limit. The Stakeholder Role Play is best conducted live during an interview. The Constrained Prompt Engineering exercise can work either way, though conducting it live gives you insight into the candidate's thinking process.

How technical should candidates be to complete these exercises?

These exercises are designed for candidates with a solid understanding of AI systems and their limitations. However, the Stakeholder Role Play specifically evaluates communication skills for non-technical audiences, which is valuable for any AI design role regardless of technical depth.

What if our company uses specific AI models or frameworks?

Feel free to adapt these exercises to reference your specific technology stack. For example, you can modify the Constrained Prompt Engineering exercise to mention your specific LLM and its known limitations, or adjust the System Architecture Planning to include your existing infrastructure components.

How should we evaluate candidates who propose unconventional solutions?

Unconventional approaches can indicate innovative thinking. Evaluate them based on whether they (1) demonstrate a clear understanding of AI limitations, (2) effectively address those limitations, and (3) meet the business requirements. The specific implementation approach may vary, but these fundamentals should be present.

Can these exercises be adapted for junior candidates?

Yes, for junior candidates, you might simplify the scenarios, provide more structure, or focus on identifying limitations rather than designing complete solutions. The Stakeholder Role Play can be particularly valuable for assessing junior candidates' understanding of basic AI limitations.

Designing for AI system limitations is a nuanced skill that combines technical knowledge, strategic thinking, and effective communication. By using these work samples, you can identify candidates who not only understand AI's theoretical constraints but can also design practical solutions that work within them. This approach helps ensure you build a team capable of delivering AI systems that are robust, reliable, and aligned with business needs.

For more resources to enhance your hiring process, explore Yardstick's suite of AI-powered tools, including our AI job descriptions generator, interview question generator, and comprehensive interview guide creator.

Ready to build a complete interview guide for evaluating AI system design skills? Sign up for a free Yardstick account today!