Interview Prep 07 — AI Coding Tools Engineer

Builds Cursor/Copilot-style coding assistants: context selection, apply-patch, autocomplete, latency-tiered routing. Phase: 11.

1. Core concepts

• IDE architecture: extension vs fork; the context engine (the hard part).
• Codebase indexing (embeddings) + symbol/AST search.
• Inline edit / apply-patch (apply model); autocomplete (FIM, latency tiers).
• Context selection under a latency budget; latency tiers force multi-model.
• Planning vs execution model routing; BYOK / provider routing.
• Code eval: apply-rate, task-resolution (SWE-bench-style), pass@k (Phase 11.08).

2. System design questions

1. Design a code assistant's context engine (which files/snippets, within a latency budget) (diagrams/11).
2. Design autocomplete (sub-second) + chat (frontier) + inline-edit tiers.
3. Design apply-patch that reliably edits the user's files.

Cursor-style diagram.

3. Debugging questions

• Autocomplete too slow → wrong latency tier; tiny FIM model; trim context.
• Suggestions ignore relevant code → context retrieval miss; symbol/AST + index.
• Apply-patch corrupts files → diff/patch robustness; validation before write.
• High cost → frontier model for everything; route by tier/difficulty.

4. Coding exercises

• Build a codebase index (embeddings) + symbol search over a repo.
• Implement FIM (fill-in-the-middle) prompting for autocomplete.
• Implement an apply-patch with validation + an apply-rate eval.

5. Architecture exercises

• Draw the Cursor-style IDE architecture + BYOK flow.
• Design the context-selection algorithm under a fixed token + latency budget.

6. Model selection questions

• Which model per latency tier (autocomplete vs inline edit vs chat/agent)?
• Planning vs execution model split for a coding agent (Phase 11.06).

7. Production incident questions

• Latency regression in autocomplete → model/context change; roll back; budget.
• BYOK key mishandled → per-user secret storage; never log (Phase 14.03).
• Code suggestions leak proprietary code to a provider → data policy/no-train; on-prem option.

8. Portfolio project

A mini coding assistant: codebase indexing + symbol search, FIM autocomplete (fast tier), an apply-patch inline-edit (mid tier), and a code eval (apply-rate / task-resolution on a few tasks). README with latency per tier + apply-rate.

9. 30-minute drill

• 5 min: context selection under latency budget; why multi-model.
• 10 min: design the context engine (whiteboard).
• 10 min: debug slow autocomplete + ignored-context suggestions.
• 5 min: code eval metrics (apply-rate, task-resolution).

10. 2-hour take-home

Build a small code-assist feature over a sample repo: index + retrieve relevant context, generate an edit as a patch, validate + apply it, and measure apply-rate + latency on a few tasks. Writeup: context strategy and latency-tier model choices.

11. Senior-level expectations

• Junior: calls a model with file content.
• Mid: indexing + retrieval + apply-patch that works.
• Senior: optimizes context selection under latency budgets, routes models by tier, and evals with apply-rate/task-resolution.
• Principal: designs the context engine + multi-model routing + eval as the product's core; handles enterprise data/BYOK/on-prem.

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