Essential Work Sample Exercises for Evaluating LLM Fine-tuning Skills

Fine-tuning Large Language Models (LLMs) for domain adaptation has become a critical skill in the AI landscape. As organizations seek to leverage the power of foundation models for specific business applications, the ability to effectively adapt these models to specialized domains can create significant competitive advantages. However, identifying candidates with genuine expertise in this area presents a unique challenge.

The technical complexity of LLM fine-tuning requires a blend of theoretical knowledge and practical implementation skills. Candidates may claim proficiency on their resumes, but without proper evaluation, it's difficult to distinguish between those who understand the concepts superficially and those who can successfully execute fine-tuning projects in real-world scenarios.

Work sample exercises provide a window into a candidate's actual capabilities, revealing their approach to problem-solving, technical depth, and ability to navigate the nuances of domain adaptation. These exercises can demonstrate whether a candidate understands the critical considerations around data preparation, hyperparameter selection, evaluation metrics, and computational efficiency.

Furthermore, LLM fine-tuning projects often require collaboration with domain experts and stakeholders. The right exercises can reveal a candidate's communication skills and ability to translate technical concepts for non-technical audiences—a crucial skill for successful implementation of these technologies in business contexts.

The following work samples are designed to evaluate candidates across multiple dimensions of LLM fine-tuning expertise. From strategic planning to hands-on implementation, these exercises will help you identify candidates who not only understand the theory but can also apply it effectively to solve real business problems through domain-specific model adaptation.

Activity #1: Fine-tuning Strategy Design

This exercise evaluates a candidate's ability to develop a comprehensive strategy for adapting an LLM to a specific domain. It tests their understanding of the fine-tuning process, their ability to identify key considerations, and their strategic thinking about model adaptation. This skill is fundamental as it determines the success of any fine-tuning project before implementation begins.

Directions for the Company:

• Prepare a brief (1-page) description of a business domain that requires LLM adaptation (e.g., legal document analysis, medical report generation, financial compliance review).
• Include specific challenges in this domain, such as specialized vocabulary, regulatory requirements, or particular formatting needs.
• Provide sample texts from the domain (3-5 examples) to give the candidate context.
• Allow candidates 45-60 minutes to complete this exercise.
• Have an AI engineer or data scientist with fine-tuning experience evaluate the response.

Directions for the Candidate:

• Review the domain description and sample texts provided.
• Develop a comprehensive fine-tuning strategy document that addresses:
• Choice of base model and justification
• Data requirements (volume, diversity, quality)
• Data preparation approach
• Fine-tuning methodology (full fine-tuning vs. parameter-efficient methods)
• Evaluation metrics and testing approach
• Computational resources needed
• Timeline and potential challenges
• Your strategy should be technically sound while also considering practical constraints like budget and time.
• Be prepared to explain your reasoning for each element of your strategy.

Feedback Mechanism:

• The interviewer should provide feedback on one strength of the strategy (e.g., "Your approach to data preparation is particularly thorough") and one area for improvement (e.g., "Your evaluation metrics could be more domain-specific").
• After receiving feedback, give the candidate 10 minutes to revise the section that needs improvement and explain their adjustments.

Activity #2: Hands-on Fine-tuning Implementation

This exercise tests a candidate's practical ability to implement fine-tuning techniques. It evaluates their coding skills, familiarity with fine-tuning frameworks, and ability to translate theoretical knowledge into working code. This hands-on skill is essential for actually executing the fine-tuning process effectively.

Directions for the Company:

• Prepare a small dataset (50-100 examples) relevant to your domain.
• Set up a development environment with necessary libraries (PyTorch, Transformers, etc.) or provide access to a notebook environment like Google Colab.
• Ensure the task is scoped appropriately for a 60-90 minute session.
• Have a technical team member available to answer clarifying questions and evaluate the implementation.
• Prepare a rubric that evaluates code quality, approach, and results.

Directions for the Candidate:

• You will be implementing a parameter-efficient fine-tuning approach (such as LoRA or P-tuning) for adapting a provided base model to a specific domain.
• Use the provided dataset to fine-tune the model.
• Your implementation should include:
• Data preprocessing
• Model configuration
• Training loop setup
• Basic evaluation
• Documentation of your approach
• Focus on writing clean, well-documented code rather than achieving perfect performance.
• Be prepared to explain your implementation choices and discuss potential improvements with more time.

Feedback Mechanism:

• The interviewer should highlight one effective aspect of the implementation (e.g., "Your data preprocessing approach was very efficient") and suggest one improvement (e.g., "Consider adding learning rate scheduling to improve convergence").
• Give the candidate 15 minutes to implement the suggested improvement or explain how they would approach it if time doesn't permit full implementation.

Activity #3: Data Preparation and Evaluation Design

This exercise focuses on the critical skills of preparing training data and designing evaluation methods for fine-tuned models. It tests the candidate's understanding of data quality issues, prompt engineering, and how to measure success in domain adaptation. These skills are vital as the quality of fine-tuning data directly impacts model performance.

Directions for the Company:

• Prepare a small set (10-15) of raw, unprocessed examples from your domain that contain various issues (inconsistent formatting, ambiguous instructions, varying quality).
• Include a brief description of the target task for the fine-tuned model.
• Provide access to a spreadsheet or text editor for the candidate to work with.
• Allow 45-60 minutes for this exercise.
• Have someone familiar with your domain and data requirements evaluate the results.

Directions for the Candidate:

• Review the provided raw examples and task description.
• Create a data preparation plan that includes:
• Cleaning and standardization approach
• Instruction/prompt template design
• Input/output formatting
• Data augmentation strategies (if applicable)
• Process at least 5 of the examples according to your plan.
• Design an evaluation framework that includes:
• Automatic metrics relevant to the domain
• Human evaluation criteria
• Test set composition recommendations
• Baseline comparison approach
• Document your reasoning for each decision in your approach.

Feedback Mechanism:

• The interviewer should provide feedback on one strength (e.g., "Your prompt design effectively captures the nuances of the task") and one area for improvement (e.g., "Your evaluation metrics don't account for factual accuracy").
• Give the candidate 10-15 minutes to revise their approach based on the feedback and explain their changes.

Activity #4: Fine-tuning Troubleshooting Scenario

This exercise evaluates a candidate's problem-solving abilities when fine-tuning projects encounter challenges. It tests their debugging skills, knowledge of common pitfalls, and ability to diagnose and resolve issues efficiently. This skill is crucial as fine-tuning projects rarely proceed without complications.

Directions for the Company:

• Prepare a detailed scenario describing a fine-tuning project that has encountered problems (e.g., catastrophic forgetting, training instability, poor performance on certain inputs).
• Include relevant logs, evaluation results, and model outputs that provide clues to the underlying issues.
• Create a document with the scenario and supporting materials.
• Allow 45-60 minutes for this exercise.
• Have a technical team member who has experience troubleshooting fine-tuning issues evaluate the response.

Directions for the Candidate:

• Review the troubled fine-tuning scenario and all supporting materials.
• Analyze the symptoms and identify potential root causes of the issues.
• Develop a systematic troubleshooting plan that includes:
• Diagnostic steps to confirm the root causes
• Potential solutions for each identified issue
• Prioritization of actions based on likely impact
• Preventive measures for future fine-tuning projects
• Document your reasoning and explain how you would implement your recommendations.
• Be prepared to discuss alternative approaches and their trade-offs.

Feedback Mechanism:

• The interviewer should highlight one effective aspect of the troubleshooting approach (e.g., "Your systematic elimination of potential causes was very thorough") and suggest one area for improvement (e.g., "Consider how the learning rate schedule might be contributing to the instability").
• Give the candidate 10-15 minutes to refine their approach based on the feedback and explain how they would incorporate this insight.

Frequently Asked Questions

How long should these exercises take in total?

Each exercise is designed to take 45-90 minutes. For a comprehensive assessment, you might spread these across multiple interview stages rather than conducting all four in a single session. Consider using the strategy exercise as a take-home assignment and the implementation exercise during an on-site interview.

Do candidates need access to powerful computing resources for these exercises?

No, the exercises are designed to be completed with modest computing resources. For the implementation exercise, consider using smaller models (e.g., distilled versions) or parameter-efficient fine-tuning methods that can run on standard hardware. Alternatively, provide access to cloud resources or focus on code quality rather than full execution.

How can we adapt these exercises for candidates with different experience levels?

For junior candidates, provide more structure and guidance in the exercises. For senior candidates, add complexity such as multi-task fine-tuning requirements or budget/resource constraints. Adjust your evaluation criteria based on the expected experience level while maintaining the core assessment of fine-tuning knowledge.

What if our company works with proprietary data that we can't share?

Create synthetic examples that mimic the characteristics of your domain without revealing sensitive information. Alternatively, use publicly available datasets from a similar domain and explain how they relate to your actual use case. The key is testing the candidate's approach rather than their familiarity with your specific data.

How should we weigh theoretical knowledge versus practical implementation skills?

This depends on your team's needs. If you have strong engineers but need strategic direction, emphasize the strategy and troubleshooting exercises. If you need someone to implement established approaches, focus on the hands-on and data preparation exercises. Ideally, evaluate both aspects to ensure the candidate can both plan and execute fine-tuning projects.

Can these exercises be adapted for remote interviews?

Yes, all of these exercises can be conducted remotely. Use collaborative coding environments for the implementation exercise, shared documents for the strategy and data preparation exercises, and video conferencing for discussions. Consider extending time limits slightly to account for potential technical issues.

LLM fine-tuning for domain adaptation requires a unique blend of theoretical knowledge, practical skills, and problem-solving abilities. By incorporating these work sample exercises into your hiring process, you'll be better equipped to identify candidates who can successfully adapt foundation models to your specific business needs. Remember that the goal is not just to test technical skills but to understand how candidates approach complex problems in this rapidly evolving field.

For more resources to improve your hiring process, check out Yardstick's AI Job Description Generator, AI Interview Question Generator, and AI Interview Guide Generator. These tools can help you create comprehensive hiring materials tailored to your specific needs.

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