How to create a code challenge that AI can't solve

Creating a good take-home coding challenge is an important part of the software engineering hiring process. The goal of a take-home challenge is to give candidates an opportunity to demonstrate their coding skills in a realistic environment, while also ensuring that the work is their own and not the result of cheating or using AI tools. Here are some tips on how to create a good take-home coding challenge and how to make it difficult for AI to solve:

Define clear objectives and constraints: Before creating a take-home challenge, it is important to define clear objectives and constraints. What skills and technologies do you want the candidate to demonstrate? What are the requirements and limitations of the project? This will help ensure that the challenge is focused and relevant to the position.
Use a real-world problem: The challenge should be designed to simulate a real-world problem that the candidate may encounter in the job. This will give them an opportunity to showcase their problem-solving skills and creativity.
Avoid trivial problems: The challenge should not be too simple or trivial, as this will not accurately reflect the candidate’s coding abilities. Instead, it should be challenging enough to demonstrate their skills, but not so difficult that it becomes discouraging or overwhelming.
Provide clear instructions and resources: It is important to provide clear instructions and resources to help the candidate understand the requirements and limitations of the challenge. This will help ensure that they are able to complete the challenge to the best of their ability.
Include evaluation criteria: The challenge should include clear evaluation criteria to help the candidate understand what is expected of them and how they will be assessed. This will help ensure that the evaluation process is fair and consistent.

To make it difficult for AI to solve or to detect if AI has been used, you can include the following:

Time-limited: A time-limited challenge will make it difficult for AI to complete the challenge in a short amount of time, as they may need to spend time training or adapting their models to the specific challenge.
Realistic data and scenarios: Using realistic data and scenarios will make it more difficult for AI to generate accurate results without access to real-world data.
Randomized elements: Including randomized elements in the challenge will make it more difficult for AI to predict or generate the correct results.
Human-centered evaluation: To detect if AI has been used, the challenge should include an evaluation process that is based on human judgment and expertise. This may include a code review or an interview with the candidate to discuss their approach and thought process.
Originality checks: To detect if the code has been copied or generated by AI, the challenge can include originality checks using tools like plagiarism detectors or machine learning models.

Overall, creating a good take-home coding challenge requires careful planning and consideration of the specific skills and abilities required for the position. By making the challenge realistic and challenging, while also incorporating measures to detect and deter the use of AI, hiring managers can ensure that they are evaluating candidates based on their actual skills and abilities.

When it comes to solving a take-home coding challenge, AI could make several types of mistakes that may make it easier for a human evaluator to detect that the work was not done by a human. Here are a few examples of the types of mistakes that an AI might make:

Misinterpretation of instructions: The AI may misinterpret the instructions given for the challenge, leading to errors or incorrect results.
Incomplete or incorrect solutions: The AI may generate incomplete or incorrect solutions that do not fully meet the requirements of the challenge.
Code structure and style: The AI may generate code with an unusual or inconsistent structure or style that is not typical of human-written code.
Lack of creativity or critical thinking: The AI may lack the creativity or critical thinking skills required to come up with innovative or optimal solutions to the challenge.
Overfitting: The AI may overfit the model to the specific dataset provided, which could result in a solution that does not generalize well to new data.

Overall, while AI can be very good at certain types of tasks and may be able to generate solutions to coding challenges, it may still make mistakes that can be detected by a human evaluator. By using a combination of evaluation methods, including automated checks and human review, hiring managers can ensure that they are accurately evaluating candidates based on their skills and abilities.

When evaluating a take-home coding challenge for a software engineering position, the hiring team should evaluate several areas of software engineering to assess the candidate’s skills and abilities. Here are a few key areas that should be evaluated:

Code quality: The team should evaluate the quality of the candidate’s code, including its readability, maintainability, and efficiency. They should look for clear and concise code that follows best practices and standards.
Technical skills: The team should assess the candidate’s technical skills, including their knowledge of programming languages, data structures, algorithms, and software design patterns. They should look for code that demonstrates mastery of these skills and the ability to apply them effectively.
Problem-solving ability: The team should evaluate the candidate’s problem-solving ability, including their ability to understand the problem, identify and analyze the key issues, and come up with effective solutions.
Creativity and innovation: The team should assess the candidate’s creativity and innovation, including their ability to come up with original solutions and approaches to problems.
Communication skills: The team should evaluate the candidate’s communication skills, including their ability to clearly document and explain their code and thought process.
Adaptability: The team should assess the candidate’s ability to adapt to new situations and technologies. They should look for evidence of the candidate’s ability to learn quickly and apply their knowledge to new problems and scenarios.
Teamwork and collaboration: The team should evaluate the candidate’s ability to work effectively in a team and collaborate with others. They should look for evidence of the candidate’s ability to communicate effectively, listen to others, and contribute to a positive team dynamic.

Overall, by evaluating these key areas of software engineering, the hiring team can gain a more comprehensive understanding of the candidate’s skills and abilities, and make an informed decision about whether they are a good fit for the role. It is important to remember that each evaluation should be based on objective and relevant criteria that are clearly defined before the challenge is issued, to ensure fairness and consistency in the evaluation process.