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Showing of 50What does it mean for a team to be "AI-native" rather than simply using AI tools?
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An AI-native team designs its delivery system around safe, repeatable AI collaboration; simply using AI means adding tools to an otherwise unchanged process.
| Dimension | Uses AI tools | AI-native |
|---|---|---|
| Workflow | Individuals prompt as needed | Specifications, context, and tests guide agents |
| Control | Existing review absorbs output | Permissions and risk gates limit actions |
| Success | Tool adoption or generated code | Delivery quality, effort, and defects |
People still own product choices, architecture, and released changes. The team maintains reusable instructions and evaluations, keeps project knowledge retrievable, and strengthens verification as generation becomes faster.
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Why should AI-generated code be reviewed at least as carefully as human-written code?
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AI-generated code deserves at least normal scrutiny because fluent syntax can hide false assumptions, invented interfaces, insecure patterns, or unintended scope.
- Read the complete diff and compare its behavior with the requirement.
- Verify unfamiliar APIs and dependencies against version-matched, authoritative sources.
- Test boundary cases, failures, authorization, data handling, secrets, and external calls.
- Review generated tests independently; they may repeat the implementation's mistake.
The submitter owns every line. Set review depth from the change's impact and reversibility, not from how quickly or confidently the code appeared.
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What is the "prompt-and-pray" anti-pattern, and what does a healthier workflow look like?
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Prompt-and-pray means sending a vague request, accepting a large result, and hoping it works without controlling scope or gathering evidence.
- Define the goal, relevant context, constraints, non-goals, and acceptance criteria.
- Ask for a plan when the work is nontrivial, then correct faulty assumptions.
- Make one small, reviewable batch and inspect its complete diff.
- Run focused checks before continuing; require approval for risky commands.
- Refine the specification or context when results are wrong.
The assistant drafts and explains work. The engineer controls direction, permissions, and the evidence required for acceptance.
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How can test-driven development act as guardrails when you delegate coding to an AI agent?
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Test-driven development gives an agent an executable contract and a short feedback loop, making delegated work narrower and easier to inspect.
- Review a test that expresses one required behavior and confirm it fails for the right reason.
- Ask for the smallest implementation that makes the test pass.
- Protect tests from unapproved edits so the agent cannot weaken the contract.
- Add boundaries, invalid inputs, and failure paths, then rerun focused checks.
- Inspect the implementation and repeat with the next behavior.
A green suite proves only the specified cases, so human review and broader verification still matter.
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What are the most common pitfalls of letting an AI generate your test suite?
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Generated tests often mirror the implementation instead of the requirement, so a polished suite and high coverage can still provide weak protection.
- Shared mistake: Code and assertions can encode the same wrong interpretation.
- Weak signal: Tautologies, excessive mocks, and snapshots may never detect realistic faults.
- Missing risk: Boundaries, invalid input, permissions, and failure paths are easy to omit.
- False confidence: Tests may pass even after the behavior they claim to protect is broken.
Start expected outcomes from an independent specification. Review assertions, add integration coverage at important boundaries, and use mutation testing where its cost is justified.
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What is a hallucinated API, and how do you catch one before it causes damage?
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A hallucinated API is an invented method, option, endpoint, or package whose plausible name makes it look real.
- Let the compiler, type checker, or language server flag unknown symbols.
- Run a focused experiment in dynamic code instead of trusting an explanation.
- Check unfamiliar calls in official documentation for the project's installed version.
- Review a package's registry, publisher, and source before installation; plausible invented names can create supply-chain risk.
- Keep new dependencies behind human approval.
When sources conflict, trust the executable project and authoritative documentation over the model's confidence.
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How does rubber-duck debugging change when the duck can talk back?
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A talking rubber duck can question assumptions, propose competing hypotheses, and suggest experiments instead of merely forcing you to explain the bug aloud.
Start with observed facts, expected behavior, and one testable hypothesis. Ask the assistant what evidence would distinguish that idea from alternatives, then run the smallest useful experiment.
Guard against anchoring on a fluent story. Share only relevant, redacted logs, and verify claims in code, documentation, or runtime output. The assistant expands the search space; direct observation decides which explanation survives.
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Why is a passing test suite weak evidence of correctness when the same model wrote both the code and the tests?
What is an "AI slop" pull request, and why do maintainers consider it harmful?
Should engineers disclose when a change was substantially AI-generated? What are the emerging norms?
What are the benefits and risks of using AI to generate documentation?
At a basic level, which design decisions should remain human-owned even on a heavily AI-assisted team?
Pull request volume doubled after your team adopted coding agents. How should review norms change?
What does "plausible-but-wrong" code look like in practice, and why do experienced reviewers miss it?
Why do AI models tend to over-engineer, and how do you counter it?
How does property-based thinking improve testing of AI-generated code?
What is mutation testing, and why is it especially valuable for auditing AI-written test suites?
How do you use an AI assistant effectively for log and stack-trace analysis?
Describe hypothesis-driven debugging with a model in the loop.
When should a large-scale change be done with a codemod versus an agent, and how do the two combine?
How do you structure incremental verification for an AI-assisted migration across a large codebase?
What does it mean to write documentation for AI consumption, and what belongs in it?
AI makes docs cheap to produce but they still go stale. How do you keep documentation fresh?
How do you evaluate whether AI-generated code is consistent with your codebase's idioms, and why does it matter?
What security issues are characteristic of AI-generated code, and how should security review adapt?
What should a company's AI usage policy for engineering actually cover?
What licensing and IP risks come with AI-generated code, and how do teams manage them?
How would you evaluate and select an AI coding tool or vendor for an engineering organization?
Why do traditional velocity metrics become misleading once AI generates much of the code?
What is spec-first development, and why has it become more prominent in AI-assisted engineering?
Design a review gate system for AI-generated changes in an organization shipping hundreds of PRs a week.
AI assistants are linked to rising code duplication and churn. How do you measure and control tech-debt accumulation?
Which engineering metrics remain meaningful in an AI-heavy workflow, and how do you instrument them honestly?
How does an experienced engineer avoid skill atrophy while working heavily with AI?
How should teams onboard and develop junior engineers when AI handles most traditional junior tasks?
What is your strategy for learning an unfamiliar stack or codebase with AI without ending up with shallow knowledge?
Explain the test oracle problem in the context of AI-generated tests, and how to mitigate it.
In an AI-heavy organization, which architecture decisions must humans own, and how do you operationalize that boundary?
When agents can generate far more code than humans can review, how do you manage the verification bottleneck?
What techniques make a very large AI-generated refactor diff actually reviewable?
How would you build and maintain a taxonomy of AI-typical defects for your review process?
Should prompts, agent configurations, and evaluation harnesses be treated as engineering artifacts? What does that mean in practice?
How do you calibrate how much to trust AI output for a given task, rather than applying uniform skepticism?
What do interviewers actually probe when they ask about your AI-assisted workflow, and what distinguishes a strong answer?
Design an organization-wide rollout of AI engineering practices for a 500-engineer company that balances velocity gains against risk.
Generation is now cheap and verification is the bottleneck. How should the software development lifecycle be re-architected around that inversion?
Under sustained high AI churn, how do you preserve long-term codebase health and human comprehension over multiple years?
AI can now complete most take-home assignments and coding screens. How would you redesign engineering hiring to get real signal?
How do you design an engineering metrics system that resists Goodhart's law specifically in an AI-amplified environment?
Looking ahead, which engineering skills should teams still develop deeply versus deliberately delegate to AI, and how do you decide?
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AI in Engineering Teams cheatsheet
- The Core Shift01
- Testing02
- Code Review03
- Debugging04
- Large-Scale Changes05
- Documentation06
- Evaluating the Quality of AI Code07
- Architecture and Design08
- Organizational Policy Template09
- Metrics10
- Skills Development11
- Interview Preparation Angle12
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