How to Design Take-Home Coding Assignments That Allow AI Tools

Mar 15, 2026

The Problem

I recently reviewed a take-home assignment submission. The code was perfect—clean, well-documented, followed all best practices. Too perfect. When I asked the candidate to explain their database schema choices, they struggled to articulate why they picked PostgreSQL over other options.

Here’s the issue: AI coding assistants are everywhere now. Banning them in take-home assignments creates artificial conditions that don’t match reality. But allowing them without structure creates a different problem—how do you evaluate actual engineering skills?

A hiring manager on Reddit captured this perfectly:

"My team has been experimenting with take home challenges and allow the use of LLMs. We make it clear that we would like to review your prompts, see a working demo, and expect you to be able to clearly speak to the architecture, why you chose one tool or technology over another."

This shift changes everything about how we assess candidates.

What Changes When AI is Allowed?

The old model tested what candidates could write from memory. The new model tests how candidates think and collaborate with AI.

Old Assessment (No AI):

Can you write correct syntax?
Do you know API patterns from memory?
Can you implement algorithms without help?

New Assessment (AI Allowed):

Can you formulate clear, specific prompts?
Do you understand architecture and trade-offs?
Can you evaluate and improve AI-generated code?
Can you make sound technical decisions?

The key insight from the Reddit discussion:

"With AI-assisted coding, there's no excuse not to include XP and SOLID engineering principles, TDD, linting, typechecking, etc into your development process."

This means engineering practices become differentiating factors, not optional extras.

The Five-Component Framework

After analyzing what works, I’ve identified five essential components for AI-allowed take-home assignments.

Component 1: Clear AI Usage Guidelines

Candidates need explicit rules. Ambiguity creates unfairness. Here’s what I include:

AI Tool Policy

ALLOWED:
- GitHub Copilot, Claude Code, Cursor, ChatGPT
- Documentation lookup and research
- Code snippet adaptation from online sources
- AI-assisted debugging

REQUIRED:
- Submit all prompts used during development
- Document your iteration process
- Explain any AI-generated code you modified

NOT ALLOWED:
- Having another person write code for you
- Submitting solutions you cannot explain
- Copying solutions without understanding

This levels the playing field. Everyone knows the rules. You create submission requirements that enable evaluation.

Component 2: Prompt Submission Requirement

This is where you see real engineering ability. Poor prompts reveal poor communication. Good prompts show technical clarity and problem decomposition.

Poor Prompt:

Write a REST API for user activity data

Good Prompt:

Create a FastAPI application for user activity analytics with:

TECH STACK:
- FastAPI for REST endpoints
- PostgreSQL with SQLAlchemy ORM
- Pydantic for data validation

ENDPOINTS:
1. POST /activities/upload
   - Accept CSV or JSON
   - Handle messy/inconsistent data
   - Validate required fields: user_id, timestamp, activity_type

2. GET /activities/stats
   - Query params: start_date, end_date, user_id, activity_type
   - Return aggregated counts and averages

DATA CLEANING:
- Normalize timestamps to UTC
- Handle missing optional fields gracefully
- Log rejected records with reasons

TESTING:
- Pytest tests for each endpoint
- Include edge cases: empty data, invalid formats, large files

The good prompt demonstrates specificity, edge case consideration, and architectural awareness. I evaluate prompts using this rubric:

Criterion	Poor	Good	Excellent
Specificity	Vague requests	Clear context	Detailed constraints
Iteration	Single attempt	Some refinement	Clear improvement loop
Technical Depth	Surface level	Some detail	Architectural awareness
Problem Decomposition	Monolithic	Some breakdown	Logical segmentation

Component 3: Working Demo Requirement

Code that doesn’t run tells you nothing. I require a 30-minute demo session:

Working Demo Session (30 minutes)

Part 1: Feature Walkthrough (10 minutes)
- Demonstrate core functionality
- Show edge cases handled
- Walk through user flows

Part 2: Architecture Deep Dive (10 minutes)
- Explain technology choices
- Discuss trade-offs considered
- Address scalability implications

Part 3: Code Review Discussion (10 minutes)
- Navigate key code sections
- Explain AI-assisted vs self-written portions
- Discuss improvements you would make

During demos, I watch for candidates who can’t explain their code. If they used AI effectively, they should understand every line. If they copy-pasted blindly, the demo exposes it immediately.

Component 4: Engineering Process Artifacts

AI handles boilerplate, so there’s no excuse for skipping engineering practices. I require:

Submission Checklist

[ ] Working application (deployed or runnable locally)
[ ] All prompts used during development
[ ] README with setup instructions
[ ] Architecture diagram or design document
[ ] Test suite with meaningful coverage
[ ] Linting and type-checking configuration
[ ] Code review notes or self-assessment

The Reddit discussion highlighted this shift:

"Our review process has started to shift one level higher where these artifacts can help us determine if the PR should be merged or not - specs, architecture diagrams, or similar planning documents."

These artifacts show process, not just output. They reveal how the candidate approaches problems.

Component 5: Follow-Up Discussion Design

The discussion is where you verify understanding. I use question banks organized by category:

Architecture Questions:
- Why did you choose [technology] over [alternative]?
- How would this scale to 10x users?
- What would you change if you had another week?
- How did you decide on the data structure?

AI Collaboration Questions:
- Show me a prompt that didn't work as expected. How did you fix it?
- What portion of this code did AI generate vs you write?
- How did you verify AI-generated code was correct?
- What did AI get wrong that you had to correct?

Process Questions:
- How did you approach testing?
- What was your debugging process?
- How did you handle unclear requirements?
- What trade-offs did you make for time?

Candidates who used AI as a tool answer these confidently. Those who let AI do the thinking struggle.

A Complete Assignment Template

Here’s a template I’ve used successfully:

Take-Home Assignment: User Activity Analytics API

CONTEXT:
You're building an API to process and analyze user activity data.
The data arrives in messy, inconsistent formats and needs to be
cleaned, validated, stored, and queried.

REQUIREMENTS:
1. REST API with endpoints for:
   - Uploading activity data (CSV/JSON)
   - Querying aggregated statistics
   - Filtering by date range, user, activity type

2. Data validation and cleaning
3. Basic error handling
4. At least 5 unit tests
5. README with setup instructions

AI USAGE:
- AI tools are allowed and encouraged
- You MUST submit all prompts used
- Be prepared to explain any AI-generated code

SUBMISSION:
- GitHub repository link
- All prompt history
- Working demo (deployed or local instructions)
- 30-minute discussion slot

TIME ALLOCATION:
Suggested 4-6 hours over 3 days
We value quality over quantity

EVALUATION CRITERIA:
- Working functionality
- Prompt quality and iteration
- Code organization and testing
- Architecture decisions and explanations
- Engineering process artifacts

Evaluation Rubric

I score submissions out of 100 points across four categories:

Take-Home Evaluation Scorecard

FUNCTIONALITY (25 points)
[ ] Core features work as specified (10)
[ ] Edge cases handled (5)
[ ] Error handling implemented (5)
[ ] Demo runs without issues (5)

PROMPT QUALITY (25 points)
[ ] Context and constraints clear (8)
[ ] Iteration visible in prompts (7)
[ ] Technical specificity demonstrated (5)
[ ] Problem decomposition evident (5)

ENGINEERING PROCESS (25 points)
[ ] Tests written and meaningful (8)
[ ] Linting/type-checking configured (5)
[ ] Code organization follows SOLID (7)
[ ] Documentation complete (5)

DISCUSSION DEPTH (25 points)
[ ] Explains technology choices (8)
[ ] Understands own codebase thoroughly (7)
[ ] Identifies improvements needed (5)
[ ] Articulates trade-offs made (5)

TOTAL: ___/100

This rubric ensures consistent evaluation across candidates while weighting AI collaboration skills appropriately.

The Onsite Alternative

For senior roles, consider a full-day onsite model that one team shared:

Onsite AI-Allowed Assessment (5 hours)

MORNING (2 hours):
- Receive poorly-defined problem with messy data
- Set up development environment
- Begin implementation with AI tools
- Encouraged to ask clarifying questions

LUNCH (1 hour):
- Informal discussion with team
- Cultural fit assessment

AFTERNOON (2 hours):
- Continue implementation
- Regular check-ins with interviewer
- Prepare presentation

PRESENTATION (30 minutes):
- Demo working solution
- Explain architecture and decisions
- Field technical questions

EVALUATION FOCUS:
- Leadership over AI dependency
- Communication throughout process
- Problem-solving approach
- Codebase familiarity in discussion

This tests working with ambiguity, seeking feedback, and taking ownership—skills that matter more than syntax recall.

Common Mistakes to Avoid

I’ve seen teams make these errors when implementing AI-allowed assessments:

Setting no guidelines: Candidates don’t know what’s allowed, creating inconsistency and fairness issues.

Ignoring prompt quality: Focusing only on code output misses the opportunity to evaluate communication and problem decomposition.

Skipping the demo: Code review without discussion can’t distinguish AI-assisted engineers from AI-dependent ones.

Not requiring engineering artifacts: Without specs, tests, and docs, you lose visibility into process and practices.

Treating AI as cheating: This misses the point. AI is a tool. Evaluate how candidates use it.

Summary

In this post, I showed how to design take-home assignments that embrace AI tools while still evaluating real engineering skills. The key point is shifting from syntax evaluation to assessing prompt engineering, architectural decisions, and code review ability.

The five-component framework—clear guidelines, prompt submission, working demos, engineering artifacts, and structured discussions—ensures you evaluate what matters in the AI era. AI is a tool, not a shortcut. Candidates who use AI effectively demonstrate strong technical communication through quality prompts, iterative problem-solving, and clear requirement translation.

Companies that update their assessments to embrace AI tools will evaluate candidates on relevant skills while identifying those who can leverage AI effectively without becoming dependent on it.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

👨‍💻 Reddit Discussion on AI-Allowed Take-Home Assignments
👨‍💻 GitHub Copilot for Technical Interviews
👨‍💻 AI-Assisted Development Best Practices

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!