Design an AI meeting notes summarizer
PremiumLLM systemEasy
- Summarization
- Speech to text
- Action items
Quick answer
This LLM system interview question asks you to design an AI meeting notes summarizer. 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 meeting notes summarizer for a video meeting product. After each call it turns the recording into a transcript, a short summary and a list of action items with owners. The hard parts are meetings longer than the model context, never inventing a decision or an owner, and handling consent and private meetings.
Notes are a record people act on. A made-up deadline or a wrong owner is worse than a missing bullet.
What must an AI meeting notes summarizer do?
- Notes after the call: Produce a 5 to 10 bullet summary, the decisions made and the action items, each with an owner and a due date only when they were said.
- Transcript links: Every bullet links to the transcript moment it came from.
How well must it do it?
- Faithfulness: No decision, number, date or owner that the transcript does not contain; at least 95% of bullets supported by their linked segment on the evaluation set.
- Latency: Notes ready within 2 minutes at p95 after a 1-hour meeting ends.
What scale should you design for?
Use these numbers in your estimates.
The scale numbers unlock with this questionUnlock
Key concepts
- Summarization
- Speech to text
- Action items
- Map reduce summarization
- Speaker diarization
- Faithfulness
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 meeting notes summarizer 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