Design an AI resume screening system
PremiumLLM systemEasy
- Fairness
- Bias audits
- Human in the loop
Quick answer
This LLM system interview question asks you to design an AI resume screening system. Aim for a design that uses language models to serve users well, then explain retrieval, prompting, serving, evaluation and trade-offs. Interviewers want to see how you control quality, cost and latency, and how you evaluate the results.
The problem
Design an AI resume screening system for a hiring platform. For each application it extracts evidence from the resume, checks it against the job's requirements and gives the recruiter a short summary with quoted proof. The hard parts are fairness across groups, resumes written to fool the model, and keeping a human in every decision.
Hiring is a high-risk use of AI. Laws such as New York City Local Law 144 require a yearly independent bias audit and notice to candidates, so the design must make audits possible from day one.
What must an AI resume screening system do?
- Requirement match: For each must-have and nice-to-have requirement, mark the application as meets, partly or not found, with a quoted line from the resume as proof.
- Recruiter summary: Show a 5-line summary and the evidence table next to the original resume.
How well must it do it?
- Fairness: The impact ratio for every group stays at or above 0.8 (the four-fifths rule) on the audit set and in production.
- Accuracy: Requirement marks agree with recruiter labels at least as often as two recruiters agree with each other.
What scale should you design for?
Use these numbers in your estimates.
The scale numbers unlock with this questionUnlock
Key concepts
- Fairness
- Bias audits
- Human in the loop
- Resume parsing
- Adverse impact ratio
- Structured extraction
How you’ll practise
- Problem framing
- Data and retrieval
- Prompting and orchestration
- Evaluation and safety
- Serving, cost and latency
- Wrap-up
Lala, personalized to you, gives a hint when you ask and reviews your design when you are ready.
What do interviewers look for?
In any LLM system design round.
- Defines the user task and what a good answer is
- Explains how relevant context reaches the model
- Plans evaluation before and after launch
- Controls cost and latency with caching and routing
- Covers safety, privacy and abuse
- Handles failures and fallbacks gracefully
Questions people ask about this interview question
- Which design type is this question?
- It is an LLM system question. You design an AI resume screening system and explain your choices the way you would in an interview.
- Is this question asked at entry level?
- It is marked Easy, so it suits engineers who are new to LLM system interviews. It is also a good warm-up before harder questions.
- What should I cover first?
- Start with the user task, what a good answer looks like and how it will be measured. Agree on scope with the interviewer before you design anything.
- Can I practise it for free?
- It is a premium question. Sign up and unlock it once for 10 credits; it then stays unlocked. When you finish, Lala, your personalized AI mentor, can review your design for 30 credits.
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Updated · Written by the SevyDevy team