Essential Work Sample Exercises for Evaluating Prompt Chaining and Complex Workflow Skills

Prompt chaining and complex workflows represent critical skills in the evolving landscape of AI implementation. As organizations increasingly rely on large language models and other AI systems to solve complex problems, the ability to design, implement, and optimize multi-step prompt sequences has become invaluable. Unlike simple one-off prompts, chained prompts create sophisticated workflows that can handle nuanced tasks requiring multiple stages of processing, refinement, and decision-making.

Evaluating a candidate's proficiency in prompt chaining requires more than theoretical knowledge assessment. The true measure of expertise lies in observing how candidates approach the design of these systems, implement technical solutions, troubleshoot issues, and adapt their strategies based on feedback. Work samples in this domain should test both architectural thinking and hands-on implementation skills.

The complexity of prompt chaining work often reveals significant differences between candidates who understand the fundamentals versus those who have developed advanced intuition through practical experience. A candidate might articulate prompt engineering principles well in an interview but struggle when faced with designing an actual workflow that maintains context across multiple steps or handles edge cases gracefully.

By incorporating the following exercises into your interview process, you can gain deeper insights into how candidates approach prompt chain architecture, implement technical solutions, optimize existing systems, and apply these skills to real business challenges. These work samples are designed to reveal not just technical competence but also problem-solving approaches, attention to detail, and adaptability—all crucial qualities for success in roles requiring prompt chaining expertise.

Activity #1: Prompt Chain Architecture Design

This exercise evaluates a candidate's ability to design a high-level architecture for a multi-step AI workflow. Effective prompt chaining requires thoughtful planning of how information flows between steps, how context is maintained, and how the system handles various scenarios. This activity reveals how candidates think about breaking down complex problems into manageable components and how they design systems that are both robust and flexible.

Directions for the Company:

• Provide the candidate with a complex business problem that requires multiple steps of AI processing.
• For example: "Design a system that helps customer service representatives respond to incoming support tickets by: 1) categorizing the ticket, 2) extracting key information, 3) retrieving relevant knowledge base articles, 4) generating a personalized response template, and 5) suggesting next actions."
• Give candidates access to a whiteboard or digital diagramming tool.
• Allow 30-45 minutes for this exercise.
• Provide information about what AI models would be available (e.g., GPT-4, a fine-tuned classification model, etc.).
• Consider providing a simple template or example of a different prompt chain architecture to establish expectations.

Directions for the Candidate:

• Create a visual diagram showing the flow of information through your proposed prompt chain.
• For each step in the chain, define:
• The purpose of that step
• The input it receives
• The prompt strategy you would use
• The expected output
• How that output feeds into subsequent steps
• Identify potential failure points and how your design addresses them.
• Be prepared to explain your architectural decisions and trade-offs.
• Consider how your design handles edge cases and maintains context throughout the workflow.

Feedback Mechanism:

• After the candidate presents their architecture, provide feedback on one strength (e.g., "I like how you maintained context between steps by…") and one area for improvement (e.g., "I'm concerned about how this design handles ambiguous requests").
• Ask the candidate to revise a specific portion of their design based on the feedback, giving them 5-10 minutes to make adjustments.
• Observe how receptive they are to feedback and how effectively they incorporate it into their revised design.

Activity #2: Implementing a Specific Prompt Chain

This exercise tests the candidate's ability to translate architectural concepts into working code or specific prompts. It evaluates technical implementation skills, attention to detail, and understanding of how to craft effective prompts that work together as a cohesive system. This activity reveals how candidates bridge the gap between theory and practice.

Directions for the Company:

• Prepare a specific prompt chaining scenario with clear requirements.
• For example: "Create a three-step prompt chain that: 1) Takes a long technical document and extracts key points, 2) Reorganizes those points into a logical structure, and 3) Generates a concise executive summary."
• Provide a sample document (1-2 pages) for the candidate to work with.
• Offer access to a development environment where they can test their prompts (this could be as simple as access to ChatGPT or a similar tool).
• Allow 45-60 minutes for this exercise.
• Prepare evaluation criteria focusing on prompt clarity, effectiveness of the chain, and quality of the final output.

Directions for the Candidate:

• Implement the specified prompt chain by writing out each prompt in the sequence.
• For each prompt, include:
• The exact text of the prompt
• Any system messages or context you would provide
• How you're incorporating outputs from previous steps
• Any parameters you would set (temperature, max tokens, etc.)
• Test your prompt chain with the provided sample document.
• Document any challenges you encountered and how you addressed them.
• Be prepared to explain your implementation choices and demonstrate your solution.

Feedback Mechanism:

• Review the candidate's implementation and provide specific feedback on one effective aspect (e.g., "Your extraction prompt is particularly well-crafted because…") and one area for improvement (e.g., "The transition between steps 2 and 3 loses some important context").
• Ask the candidate to revise one specific prompt based on your feedback.
• Give them 10-15 minutes to implement the change and test the revised chain.
• Evaluate how they incorporate the feedback and whether the revision improves the overall result.

Activity #3: Debugging and Optimizing an Existing Prompt Chain

This exercise assesses a candidate's ability to analyze, troubleshoot, and improve an existing prompt chain system. It tests critical thinking, diagnostic skills, and the ability to optimize for better performance. This activity reveals how candidates approach problem-solving in complex systems and their attention to both details and overall system performance.

Directions for the Company:

• Create a prompt chain with intentional flaws or inefficiencies.
• For example: A four-step workflow that: 1) Extracts data from customer emails, 2) Classifies customer sentiment, 3) Generates response options, and 4) Formats the final response.
• Include specific issues such as:
• A prompt that loses important context between steps
• Inefficient prompt design that requires unnecessary tokens
• A step that occasionally produces formatting inconsistencies
• A classification step with ambiguous instructions
• Provide sample inputs that will trigger these issues.
• Include documentation of the current system and its intended function.
• Allow 45-60 minutes for this exercise.

Directions for the Candidate:

• Review the provided prompt chain and its documentation.
• Test the system with the sample inputs to observe its behavior.
• Identify at least three specific issues or opportunities for improvement.
• For each issue:
• Describe the problem and its impact
• Explain your diagnosis of the root cause
• Implement a specific fix or optimization
• Test your solution to verify the improvement
• Document your changes and the reasoning behind them.
• Be prepared to discuss additional improvements you would make with more time.

Feedback Mechanism:

• After the candidate presents their optimizations, highlight one particularly effective fix they implemented (e.g., "Your solution to the context loss problem was elegant because…").
• Point out one area where their approach could be further improved or where they missed an opportunity for optimization.
• Give them 10-15 minutes to implement this additional improvement.
• Evaluate their ability to quickly iterate on their solution and their understanding of the feedback.

Activity #4: Real-World Application Challenge

This exercise evaluates how candidates apply prompt chaining skills to solve an authentic business problem. It tests their ability to translate requirements into a working solution, balance technical constraints with business needs, and create systems that deliver real value. This activity reveals how candidates think about the practical application of prompt chaining in business contexts.

Directions for the Company:

• Present a realistic business scenario that would benefit from a prompt chaining solution.
• For example: "Our marketing team needs to analyze customer reviews across multiple platforms to identify trending topics, sentiment patterns, and opportunities for product improvement. Design and implement a prompt chain that processes batches of reviews and generates actionable insights reports."
• Provide a dataset of 10-15 sample customer reviews from different sources.
• Specify any constraints (e.g., cost considerations, performance requirements, integration needs).
• Allow 60-90 minutes for this more comprehensive exercise.
• Prepare evaluation criteria that include both technical implementation and business value.

Directions for the Candidate:

• Analyze the business requirements and define specific objectives for your prompt chain.
• Design an end-to-end workflow that addresses the business need.
• Implement at least two key components of your proposed solution as working prompts.
• Test your implementation with the provided sample data.
• Prepare a brief explanation of:
• Your overall approach and architecture
• How your solution addresses the business requirements
• Technical considerations and trade-offs
• How you would measure the success of your solution
• How your solution could scale or evolve as needs change
• Be prepared to demonstrate your partial implementation and explain how you would complete the full system.

Feedback Mechanism:

• Provide feedback on both the technical aspects of the solution and its alignment with business needs.
• Highlight one particularly strong aspect of their approach (e.g., "Your method for categorizing topics across different review formats is particularly robust").
• Suggest one significant way their solution could better address the business requirements.
• Give them 15-20 minutes to revise their approach based on this feedback.
• Evaluate how well they balance technical considerations with business value in their revised solution.

Frequently Asked Questions

How technical should candidates be to complete these exercises?

Candidates should have experience with prompt engineering and understand how language models work, but they don't necessarily need to be programmers. The exercises focus on designing prompts and workflows rather than complex coding. That said, familiarity with APIs, JSON, and basic scripting would be beneficial for implementing real-world prompt chains.

Should we provide access to specific AI models during the exercises?

Yes, ideally candidates should have access to the same or similar models they would use in the role. At minimum, provide access to a general-purpose model like ChatGPT or Claude. For more specialized roles, consider providing access to the specific models or tools they would be working with. If access isn't possible, you can modify the exercises to focus more on design than implementation.

How do we evaluate candidates who take different approaches to the same problem?

Focus on the reasoning behind their decisions rather than expecting a specific "correct" solution. Evaluate whether their approach is logical, addresses the requirements, anticipates potential issues, and demonstrates an understanding of prompt chaining principles. Different approaches can be equally valid if well-executed and well-reasoned.

Can these exercises be conducted remotely?

Yes, all of these exercises can be adapted for remote interviews. Use collaborative tools like Miro or Figma for the architecture design, screen sharing for implementations, and video conferencing for presentations and feedback. Consider extending the time slightly for remote exercises to account for potential technical issues.

How much domain knowledge should candidates have about the business problems in these exercises?

The exercises should focus on prompt chaining skills rather than deep domain expertise. Choose business scenarios that are relatively accessible or provide sufficient context information. If the role requires specific domain knowledge, you can incorporate more specialized scenarios, but be sure to evaluate candidates primarily on their prompt chaining abilities.

Should we expect perfect solutions within the time constraints?

No, these exercises are designed to reveal thinking processes and approaches rather than perfect implementations. Look for candidates who make reasonable trade-offs given the time constraints, focus on the most critical aspects of the problem, and can articulate what they would improve with more time.

Prompt chaining and complex workflows represent a specialized skill set that's becoming increasingly valuable as organizations seek to leverage AI more effectively. By incorporating these practical work samples into your interview process, you can identify candidates who not only understand the principles but can apply them to create robust, effective solutions to real business problems.

The best candidates will demonstrate not just technical proficiency but also strategic thinking about how prompt chains should be designed, implemented, and optimized. They'll show attention to both the details of individual prompts and the overall architecture of the system. Most importantly, they'll demonstrate the ability to translate business requirements into working AI solutions that deliver tangible value.

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