How the interviewer opens it
“Design an AI coding assistant that suggests code inside an editor. It uses editor and repository context. It must respond quickly and protect private source code.”
The prompt is vague on purpose. The first minutes are for you. Reduce the prompt to the core features and agree on the numbers before you draw.
What to clarify first
- Completion traffic
- Repository size
- Read and write mix
- Latency
- Model limits
- Editor context
- Consistency and availability
- Private source processing
- Retention and durability
- Permissions and exclusions
The interviewer has the answers. In a SysRound round, Alex gives these answers, as a real interviewer does.
Scope of a strong answer
Core requirements
- Suggest the next few lines at the cursor
- Use relevant editor text and authorized repository context
- Keep suggestions responsive as the user edits code
- Protect private source throughout its use and retention
Usually out of scope
- Chat and autonomous repository edits
- Code execution
- Model pretraining
- Repository hosting
- Search across unrelated repositories
What interviewers ask about
- Context for a suggestion
- A small prompt must capture useful code from a large repository. Unsaved editor text may differ from every stored revision.
- Time before a suggestion
- Model queues and token generation share a tight response budget. Large prompts can delay many short requests.
- Edits during a request
- The user can change the document before a suggestion arrives. Obsolete requests also consume scarce inference capacity.
- Private source code
- Source can leak through context access, caches, logs, or inference. Old repository copies can retain code after access changes.
Common mistakes
- The design sends the whole repository with each request.
- Capacity estimates count requests but ignore input and output token demand.
- Latency tests treat the first generated token as a useful suggestion.
- The editor displays obsolete results without checking document versions.
- The privacy design relies on encryption alone.
- The design promises to use unsaved edits that the editor never supplies.
The bar by level
- Senior (L5/E5)
- Budgets latency across the full request. Defines revision checks and bounded unsaved dependency context. Handles cancellation, overload, current permissions, and source deletion with concrete failure behavior.
- Staff (L6+/E6+)
- Derives fleet capacity and cost from measured token throughput. Defines revocation and regional failure boundaries. Explains how context quality tests respect privacy. Sets explicit trade-offs among acceptance, latency, isolation, and cost.
Sources · 1 reference
These records explain where the question appears. A company tag or candidate report does not guarantee a future interview question.
SysRound · 1 record
No company association
Design an AI coding assistant like GitHub Copilot
Reported date not specified
Source snapshot: Nov 12, 2026
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