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THE WORK, NOT THE HYPE

Software Engineering with AI.

Explore how AI changes your work — and what to learn next.

A practical guide to
the work ahead
Software EngineeringRefactoringGrowing Practice

Refactoring

Suggest restructuring and draft mechanical edits.

The workflow, side by side

Traditional workflow

Before generative AI assistance
  1. 1

    Clarify the goal using a duplicated tax-calculation module

  2. 2

    Capture current behavior with tests

  3. 3

    Extract shared logic in small changes

  4. 4

    Check callers and compare outputs

  5. 5

    Check the result against the agreed criteria

  6. 6

    Communicate the outcome and record the decision

AI-assisted workflow

AI contributes. You guide and verify.
  1. 1

    Define the goal, constraints, and permitted information

  2. 2

    Provide relevant, sanitized context from a duplicated tax-calculation module

  3. 3

    Ask AI to suggest restructuring and draft mechanical edits

    AI + YOU
  4. 4

    Inspect suggestions against original evidence and domain rules

  5. 5

    Revise the output and independently validate the result

    YOU
  6. 6

    A responsible professional approves and communicates the outcome

HUMAN CHECKPOINT

Is externally observable behavior preserved for every supported case?

Your judgment matters

The shift: Reviewing, validating, and integrating AI-generated code. Foundational skills still matter.

Build the skills behind the work.

A practical learning path for Software Engineering.

GO A LITTLE DEEPEROpen only what you need
What changes — and what doesn’tSkills & responsibilities
AI HELPS WITH
GenerateReview

Suggest restructuring and draft mechanical edits. The output is a starting point to inspect, not a decision to accept automatically.

STILL YOUR RESPONSIBILITY

Validate correctness, choose tradeoffs, protect users, and approve changes.

Skills to develop

FoundationsProgramming, algorithms, system design, and security.

AI collaborationProviding task-specific context and requesting explicit assumptions.

VerificationChecking a duplicated tax-calculation module against independent evidence.

Professional skillsCommunicating tradeoffs and taking responsibility.

Where AI can go wrong3 things to check

A plausible but wrong answer

A cleaner abstraction may change rounding order. It can fail the underlying goal even when it sounds convincing.

Your check

Compare invoice totals against existing rounding requirements.

Missing or invented context

AI may fill gaps with unsupported assumptions, which can send the work in the wrong direction.

Your check

Trace claims to original evidence and ask the relevant person about unknowns.

Information shared in the wrong place

Sensitive records or code can cross confidentiality boundaries if supplied to an unsuitable tool.

Your check

Use approved tools, share the minimum context needed, and follow your organization’s rules.

Try a quick exerciseA practical scenario

AI combines two tax calculations but rounds only at the end instead of per line.

What is the most important next step in this scenario?
Sources & contextEvidence behind this example

These are illustrative workflows, not claims that AI is always better or that every organization works this way. The scenarios and checkpoints are editorial teaching examples.

Reviewed September 2026 · Growing PracticeO*NET — Software DevelopersSupports the role and task baseline; it does not validate our AI workflow sequences.GitHub — Responsible use of inline suggestionsDocuments review and security responsibilities for generated code; capabilities vary by tool and configuration.GitHub — About Copilot code reviewEvidence of an available assisted-review capability, not proof of universal adoption or correctness.How we build these examples