AI-Assisted Engineering Still Depends on Human Judgment

Adapted for Binomial from Ravi Singh’s Coding with AI Is like Guitar Hero, originally published December 28, 2025.

An engineer can now assemble a convincing feature quickly: a working interface, an endpoint, and a set of tests. The demonstration may succeed while questions about authorization, failure handling, or long-term maintenance remain unanswered. The next review needs to establish what the engineer understands about the system they are changing.

Ravi Singh uses a music-game analogy to describe the gap between performing a pattern and mastering the underlying craft. Applied to AI-assisted engineering, the distinction is useful. Fluency with a tool can improve execution, but someone still needs to evaluate the design, recognize weak assumptions, and own the result.

Make explanation part of completion

Ask the author to explain the change’s intent, the important boundaries, and the alternatives they rejected. This does not require a lengthy design document for every task. A small fix may need a few clear sentences; a cross-service change needs enough detail for another engineer to challenge the design.

For an authorization change, the explanation should identify who can perform the action, where tenant scope is enforced, and what happens when access is denied. A green demonstration of the happy path cannot answer those questions. Neither can a polished summary generated from the same implementation.

Reviewers should compare the explanation with the actual behavior. If they cannot reconcile the two, the task needs investigation before completion. The author remains responsible even when a model drafted most of the code.

Test the assumptions that make the change safe

AI can help produce tests, but a test suite can repeat the implementation’s assumptions. Start with the behavior that matters to users and operators. Identify what should happen when input is invalid, an upstream call fails, a request is repeated, or permissions differ.

For example, a payment integration that passes once may still behave incorrectly when the provider times out after accepting a request. Review the retry behavior and how an uncertain outcome is represented. That question comes from the system’s contract, not from the number of generated test cases.

Use automated checks where they give reliable evidence. Reserve manual review for the architectural choices and operating conditions that those checks do not cover. Record the gap explicitly so a successful test run does not become a broader claim than it supports.

Keep design decisions visible

Models can propose multiple locally reasonable solutions. A team needs a consistent reason to prefer one: existing conventions, dependency limits, domain boundaries, or the need to make future changes easier. Put those constraints into the task context and check them during review.

If an implementation adds a new library for a capability the repository already has, ask whether the addition earns its maintenance cost. If it introduces a second representation of the same business concept, examine why the existing model was insufficient. These are engineering decisions that remain necessary when drafting becomes faster.

Useful judgment also includes knowing when to stop generating. Repeated patches without a stable explanation of the failure can widen a change and make it harder to review. Return to the reproduction, evidence, and intended behavior before adding another fix.

Coach for understanding, not visible activity

Managers can use specific changes as coaching material. Ask the engineer to walk through the data path, name the likely failure modes, and explain how they would investigate an incident. The purpose is to identify where practice or support would improve their effectiveness.

Pair an engineer with someone who understands the relevant system. Review an accepted suggestion, a rejected suggestion, and the reasoning behind each. Share examples that reveal a reusable habit, such as supplying an existing interface as context or writing a failing behavioral check before asking for a fix.

• Before generation: define the behavior, constraints, and acceptance checks.
• Before review: explain the design and inspect the actual diff.
• Before release: verify important failure paths and identify an owner.
• After delivery: revisit corrections and share what the team learned.

Use evidence to direct the next conversation

The AI Engineering Readiness Assessment can frame coaching and codebase priorities around scoped engineering evidence. Review delays or repeated fixes can identify work worth examining, but they do not establish an engineer’s competence without context.

Repository records alone cannot reveal how much someone understood, how they prompted a model, or which tool they used. Bring the author’s explanation, review evidence, and relevant validation into the assessment. Effective AI engineering combines faster execution with the judgment to recognize whether the result is ready to own.