How I Build — AI-Assisted Engineering Workflow

Spec

Orchestrate

Verify

Iterate

Audit

Judge

Specification 0:00

The prompt is the most critical part. Specificity, constraints, and acceptance criteria — catching contradictions before any code is written.

Orchestration 0:08

Directing the AI effectively. The first attempt looks plausible but has a subtle edge case — the dual scoring threshold allows one scorer to pass with a 6.9 if the other scores 8.6.

Verification 0:45

Write the test that catches the bug before fixing it. Proving it works, not hoping it works.

Iteration 1:10

Tight feedback loops. The fix is a 2-line guard clause.

quality_gate.py — 1 file changed, 1 insertion(+), 1 deletion(-)

                            41
                             
                                 avg = (mean(scores_a) + mean(scores_b)) / 2
                        
                            42
                            -
                                 if avg >= 7.75:
                        
                            42
                            +
                                 if avg >= 7.75 and min(mean(scores_a), mean(scores_b)) >= 7.0:
                        
                            43
                             
                                     return {"decision": "PASS", "score": avg}

Verification at Scale 2:20

The 283-probe system catches what unit tests miss. Running the quality gate across all 86 articles reveals a clamp bug.

Waiting for replay...

Judgment 3:00

Knowing when output is good enough. The quality gate is live — and it rejects most of what the pipeline produces. That's the point.

Specification Orchestration Verification Iteration Verification at Scale Judgment

3 minutes from specification to production. The AI writes the code. The engineer writes the tests.

The AI-Assisted Engineering Workflow