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From LeetCode to Real-World: Rethinking Assessment Design

Algorithm puzzles don't predict job performance. We share our approach to designing assessments that mirror actual development work.

Ragavendra Selvam·Founder & CEO, InterviewLM
December 24, 2024
5 min read

Everyone knows the feeling: you spend weeks grinding LeetCode problems, ace the technical interview with a clever dynamic programming solution, then start the job and never use any of it again.

There's a better way.

The LeetCode Problem

Algorithm puzzles have dominated technical hiring for decades. They're popular because they're:

  • Easy to administer
  • Objective to grade
  • Familiar to interviewers

But they have serious problems:

Low Predictive Validity Research consistently shows weak correlation between algorithm puzzle performance and job performance. You're testing a skill candidates rarely use.

Bias Toward Preparation Candidates who can afford time and resources to grind problems have a massive advantage, regardless of actual engineering ability.

Missing Real Skills Reading documentation, debugging existing code, working with APIs, collaborating with others—none of this appears in a typical LeetCode assessment.

What Real Development Looks Like

Think about what engineers actually do day-to-day:

  • Read and understand existing code
  • Make changes to unfamiliar codebases
  • Debug issues from error messages
  • Integrate third-party APIs
  • Write code that others can maintain
  • Collaborate with teammates and tools

Why aren't we testing these skills?

Our Assessment Design Philosophy

At InterviewLM, we design assessments around realistic work scenarios:

1. Start With a Codebase

Instead of a blank file, candidates start with existing code. This tests:

  • Code reading comprehension
  • Understanding of existing patterns
  • Ability to extend without breaking

2. Real Requirements, Not Puzzles

Instead of "implement optimal pathfinding," we ask things like:

  • "Add user authentication to this Express app"
  • "Fix the bug causing data to display incorrectly"
  • "Implement the feature described in this ticket"

3. Tools They'd Actually Use

Candidates have access to:

  • AI coding assistants (monitored)
  • Documentation
  • Package managers
  • Realistic development environment

4. Time for Thinking

90 minutes to 2 hours instead of 45-minute sprints. Real development isn't speed coding under pressure.

Example: A Realistic Assessment

Here's what one of our assessments looks like:

Scenario: You're joining a team that maintains a REST API for a task management application. Your first ticket is to add a "due date" feature to tasks.

Starting Point:

  • Working Express.js application with existing endpoints
  • SQLite database with tasks table
  • Basic test suite
  • README with setup instructions

Requirements: 1. Add a due_date field to tasks 2. Allow setting due date when creating/updating tasks 3. Add an endpoint to list overdue tasks 4. Update tests to cover new functionality

Evaluation: We assess not just whether it works, but:

  • Code quality and consistency with existing patterns
  • Test coverage and quality
  • How they used AI assistance
  • Database migration approach
  • API design decisions

The Results

Since switching to realistic assessments, our customers report:

  • Better signal: Hiring managers feel more confident in decisions
  • Happier candidates: Developers prefer assessments that feel like real work
  • Faster onboarding: New hires demonstrate skills they'll actually use
  • Improved diversity: Less bias toward specific preparation patterns

Building Your Own Realistic Assessments

If you're not using InterviewLM, here are principles you can apply:

1. Use Real Code Don't start from scratch. Give candidates existing code to work with.

2. Write Story-Based Requirements Frame requirements as you would in a real ticket, not as abstract problems.

3. Allow Resources Let candidates use documentation and tools. Focus on results, not memorization.

4. Evaluate Process, Not Just Output How they approach the problem matters as much as the final code.

5. Give Adequate Time Rushed assessments favor test-takers over thoughtful developers.

The Bottom Line

Technical assessments should predict job performance. If your assessment tests skills candidates won't use and ignores skills they will, it's time for a change.

Real development is messy, collaborative, and tool-assisted. Your assessments should be too.


Experience realistic assessments for yourself. [Start your free trial](/auth/signup) and see the difference.

About the author

Ragavendra Selvam·Founder & CEO, InterviewLM

Ragavendra is the founder of InterviewLM. He writes about AI-native hiring, the 4-dimension AI-collaboration rubric, and the engineering decisions behind InterviewLM's sandbox, voice, and evaluation stack.

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