The actual coding part was less stressful than I expected because the scaffolding was already there.
Start by emphasizing that preserving observable behavior is the top priority, so you would first build a comprehensive test suite that captures all inputs and outputs of the current game. Then, use AI coding tools to generate a refactored version, but only after establishing clear constraints and validating every change against the tests. Finally, discuss how you would iteratively improve the design while ensuring no regressions occur.
Pro tip: Mention that you would use AI tools to generate the test suite itself from the existing code, then manually review and augment it to cover edge cases, because AI-generated tests can miss subtle behaviors. This shows you leverage AI but don't blindly trust it.
Run the existing game, document all inputs, outputs, and state transitions, and create a comprehensive test suite that captures every observable behavior. Use AI to help generate tests from the code, but verify them manually.
Decide what aspects to improve (e.g., modularity, readability, extensibility) while strictly maintaining identical external behavior. Set clear boundaries for the AI tool, such as 'do not change any output strings or control flow logic that affects user experience.'
Use AI coding tools to propose a refactored implementation, providing the original code, the test suite, and explicit constraints. Iterate with the AI, reviewing each change and running tests after every modification.
Run the full test suite against the refactored code. If any test fails, analyze the discrepancy, fix it (manually or with AI assistance), and repeat until all tests pass. Also perform exploratory testing to catch any missed behaviors.
Once behavior is preserved, consider further improvements like replacing heuristic rules with a more maintainable architecture (e.g., state machine, data-driven design) while keeping tests green. Discuss how AI can assist in this ongoing process.
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Frame your answer as a structured, iterative process that starts with clarifying the task and constraints, then moves through decomposition, prompt design, evaluation, and refinement. Emphasize how you balance technical trade-offs (e.g., specificity vs. flexibility, cost vs. quality) and adapt to ambiguity by testing and learning. Use a concrete example to illustrate your decision-making.
Pro tip: Show that you treat prompting as an engineering discipline: define measurable success criteria upfront, version your prompts, and use systematic evaluation (e.g., A/B tests, error analysis) to drive improvements. This demonstrates maturity and aligns with Cohere's focus on production-grade ML.
Start by understanding the goal, success metrics, and constraints (e.g., latency, cost, accuracy). Ask clarifying questions if needed to resolve ambiguity.
Break the task into sub-tasks or steps that the LLM can handle reliably. Decide which parts require prompting vs. other approaches (e.g., retrieval, fine-tuning).
Craft a prompt that includes clear instructions, context, and examples (few-shot) if helpful. Choose the order of information to maximize clarity and performance.
Test the prompt on a diverse set of inputs, measure against success criteria, and analyze failures. Refine the prompt iteratively, adjusting wording, examples, or structure.
Balance performance with cost, latency, and maintainability. Consider techniques like prompt compression, caching, or fallback strategies, and document decisions.
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Talked through the diffing approach and mentioned a few scenarios I manually walked through.
Structure your answer around a systematic verification process, emphasizing both quantitative metrics and qualitative checks. Be transparent about the limitations of your verification and how you addressed residual uncertainty. Highlight the trade-offs between thoroughness and practicality.
Pro tip: Show that you understand the difference between statistical equivalence and practical equivalence—sometimes a new implementation is 'equivalent enough' for production, but you should articulate the risks and how you mitigate them.
Explain how you determined what 'equivalent' means for this system—e.g., same outputs within tolerance, same performance metrics, same behavior on edge cases. This sets the stage for your verification strategy.
Describe the test suite you created: unit tests for components, integration tests for end-to-end behavior, and statistical tests (e.g., A/B comparison, hypothesis testing) for ML models. Mention any use of golden datasets or shadow deployment.
Walk through how you ran the tests, what metrics you tracked (e.g., accuracy, latency, resource usage), and how you interpreted discrepancies. Highlight any automated tooling or dashboards used.
Discuss specific areas where you had doubts (e.g., rare inputs, numerical stability, model drift) and how you investigated them. Explain any additional experiments or manual reviews you performed.
Summarize your overall confidence, acknowledging any remaining uncertainties. If applicable, describe how you planned to monitor the new implementation post-deployment to catch issues.
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This was actually the most interesting part of the conversation.
Choose a concrete project where you used an LLM, and walk through specific instances where you trusted its output versus overrode it. Focus on the decision criteria you used—such as task criticality, confidence signals, and cost of errors—and how you balanced efficiency with quality.
Pro tip: Frame your overrides as systematic risk management, not ad-hoc fixes: show you have a mental model for when LLM output is reliable (e.g., low-stakes, high-consistency tasks) versus when human judgment is essential (e.g., safety-critical, ambiguous, or novel situations).
Briefly describe the project, the LLM's role, and the stakes involved. This helps the interviewer understand the environment in which you made trust decisions.
Describe a specific case where you accepted the LLM's output. Explain why: e.g., the task was well-defined, the output was verifiable, or the cost of error was low.
Describe a specific case where you overrode or manually fixed the LLM's output. Explain what triggered the override: e.g., factual inconsistency, safety concern, or domain-specific nuance.
Articulate the criteria you used to decide when to trust or override, such as confidence scores, task criticality, and the availability of ground truth.
Summarize lessons learned and how you apply them to future LLM interactions, showing growth and adaptability.
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Talked about regression test coverage, human review gates, canary deployments.
Frame the answer around a layered defense-in-depth strategy: start with pre-commit static analysis and type checking, then add LLM-specific guardrails like constrained decoding and semantic diff review, and finish with progressive rollout and automated rollback. Emphasize that the goal is to make LLM refactoring safe by default, not to trust the model's output.
Pro tip: Propose a 'semantic diff' review step where you compare the AST and behavior of the original and refactored code, not just the text diff, and gate merges on property-based tests that assert equivalence. This shows you understand that LLMs can produce syntactically valid but semantically different code.
Use structured generation (e.g., JSON schema, grammar-constrained decoding) and provide the model with the exact refactoring rules, type signatures, and test cases. This reduces hallucination and ensures the output is parseable and type-correct.
Run linters, type checkers, and a semantic diff tool that compares the AST and control-flow graph of the original and refactored code. Flag any structural changes that aren't purely syntactic.
Require that all existing tests pass and add property-based tests that assert input-output equivalence between the original and refactored functions. Use mutation testing to ensure the tests are strong enough to catch subtle regressions.
Merge refactorings behind feature flags or in small batches, monitor error rates and performance in staging and canary environments, and set up automatic rollback on anomalies.
Require code owner approval for LLM-generated changes, and feed rejected or corrected diffs back into a fine-tuning or prompt-improvement loop to continuously harden the system.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.