Take-home Assignments
Take-home Assignments are tasks or projects given to candidates to complete on their own time as part of the job application process. These assignments are designed to assess a candidate's skills, problem-solving ability, and compatibility with the job's responsibilities.
Goal
The primary goal is to evaluate the candidates' abilities in a real-world context, allowing them to demonstrate their skills and creativity in a low-pressure environment. It also gives candidates a taste of the kind of work they would be doing.
Context
Interviewing is a skill. But you do not need the same skill set to interview as you do to do the job. Take-home assignments can help to bridge this gap. They can also help to reduce bias in the interview process. By providing a structured task that all candidates complete, you can ensure that all candidates are evaluated on the same criteria.
Assignment Types
| Type | Description |
|---|---|
| Coding Challenges | Tasks that require the candidate to write code, fix bugs, or design algorithms relevant to the job's technical needs. |
| Design Projects | Assignments for design roles, asking candidates to create or critique designs, illustrating their design thinking and execution skills. |
| Case Studies | For strategy or business roles, candidates analyse a problem, review data, and propose solutions, demonstrating their analytical and strategic thinking. |
| Presentation Tasks | Candidates prepare a presentation on a given topic, showcasing their ability to communicate complex ideas clearly and persuasively. |
Inputs
| Artifact | Description |
|---|---|
| Assignment Brief | A detailed description of the assignment, including objectives, scope, criteria for success, and deadlines. |
| Shortlisted Candidates | A list of candidates who meet the initial criteria and are selected for further evaluation. |
Outputs
| Artifact | Description | Benefits |
|---|---|---|
| Shortlisted Candidates | A list of candidates who meet the initial criteria and are selected for further evaluation. | Streamlines the recruitment process by focusing on qualified candidates. |
| Feedback for Rejected Candidates | Constructive feedback provided to candidates who were not selected for further consideration. | Enhances employer brand and candidate experience. |
Designing assignments that account for AI
AI assistants make it trivial for a candidate to produce a polished take-home submission without doing much of the underlying thinking. This is not a reason to drop take-home assignments. It is a reason to design them differently.
A good approach:
- Assume candidates will use AI. Just like calculators in maths exams, banning AI is unenforceable. Instead, design the assignment so AI is a useful tool but not a substitute for the candidate's judgment.
- Pair the take-home with a follow-up walkthrough. Ask the candidate to talk through their thinking: what trade-offs they considered, what they would do differently, what they would do next if they had more time. Candidates who relied on AI without engaging with the problem cannot answer these well.
- Look for taste, not output. A take-home that produces beautiful UI is now table stakes. What is rare is judgment: did the candidate scope the problem well? Did they push back on assumptions in the brief? Did they make a deliberate trade-off and articulate it? These are signals AI cannot fake.
- Keep the time budget tight. If the assignment takes more than 2–3 hours of human time, you are filtering for who has the most spare time, not the best fit.
Anti-patterns
- Overly Time-consuming Assignments: Giving candidates tasks that require an unreasonable amount of time to complete can discourage them from continuing the application process.
- Failure to Query Candidates: With modern LLMs it is easy to cheat on take-home assignments. It is important to ask candidates to explain their work and thought process.
- Penalising AI use directly: Telling candidates "do not use AI" is unenforceable and signals that the team is behind on how work actually gets done. Design assignments that benefit from AI as a tool while still showing the candidate's judgment.