Design an insurance claims AI agent
PremiumLLM systemMedium
- RAG
- Document extraction
- Model tiering
Quick answer
This LLM system interview question asks you to design an insurance claims AI agent. 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
An insurance claims AI agent reads each claim's forms, invoices, emails and photos, checks the facts against the claimant's policy wording with RAG, and recommends a decision with cited clauses. The hard part is doing this on messy, multimodal input for about $0.05 a claim, while code owns the money and a human signs every denial.
The agent works for a property and auto insurer. Claimants and brokers submit claims through a portal, an API or by email, attaching scanned claim forms, repair estimates, invoices, police reports and photos of the damage. The agent must extract the facts of each claim, check them against the claimant's own policy wording (base form, endorsements and declarations page) using retrieval-augmented generation (RAG), and produce a recommended decision: approve, approve in part, deny, or ask for more information, with a payout amount and the policy clauses that justify it.
A wrong approval leaks money; a wrong denial harms a customer and creates regulatory exposure. Every decision must be explainable to an adjuster, an auditor or a court years later, and the token bill grows with every page you send to a model. It has been reported at Scale AI in candidate interviews.
What must an insurance claims AI agent do?
- Decision recommendation: Output one of approve, partial, deny, needs_info or refer, with a payout amount, cited policy clauses and plain-language reasons.
- Policy check: Check the claim against the forms, endorsements and declarations of the claimant's policy that were in force on the date of loss.
How well must it do it?
- Cost: Average model plus OCR spend is at most $0.05 per claim, with a hard cap of $0.30 for any single claim.
- Accuracy: Fewer than 0.5% of automatically paid claims would be reversed by an adjuster on audit.
What scale should you design for?
Use these numbers in your estimates.
The scale numbers unlock with this questionUnlock
Key concepts
- RAG
- Document extraction
- Model tiering
- Structured output
- Human in the loop
- Confidence calibration
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 insurance claims AI agent and explain your choices the way you would in an interview.
- Is this question asked at entry level?
- It is marked Medium. Entry-level engineers can practise it, but it expects you to reason about requirements and trade-offs, not only list components.
- 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.
Related questions
Updated · Written by the SevyDevy team