Input Quality: Context Is King

AI can’t read your mind. When I architect systems, I don’t ask “design a microservices architecture.” I provide context: scale requirements, team capabilities, infrastructure constraints, regulatory requirements based on the region, budget, timeline.

Bad: “Create a database schema for customer data.”

Good: “Design a PostgreSQL (for latest stable version) schema for B2B SaaS customer data. 10K organizations, 500 users each. Multi-tenancy with data isolation for compliance. Sub-100ms reads on lookups. EU/US data residency requirements.”

The second input gives AI the constraints that matter. It generates something useful rather than generic boilerplate.

Output Validation: Your Domain Expertise Is Irreplaceable

AI doesn’t know what it doesn’t know. It will confidently generate solutions that violate fundamental principles or ignore real-world constraints.

We recently asked AI to design a caching strategy for a high-traffic API. The response was textbook-perfect – Redis, TTL strategies, cache invalidation. But it missed something critical: our financial data couldn’t tolerate stale cache due to regulatory requirements. My 27 years with financial systems immediately flagged this. A junior engineer might have implemented it as-is.

Research backs this up: 45% of AI-generated code contains security vulnerabilities. After five refinement iterations, critical vulnerabilities actually increased by 37.6%.[4][5] AI makes code look right – but looking right isn’t being right.

Iterative Refinement: Where Magic Happens (With Human Guidance)

The first output is rarely the best output – but here’s the critical distinction: AI iterating on its own code without human validation actually makes things worse, as we saw with security vulnerabilities increasing. However, when human expertise guides each iteration, that’s where the real breakthroughs happen.

Recently, we built an early warning system for a financial services client to predict loan default risk. The initial AI model achieved 72% accuracy – decent, but not good enough for production where false negatives could cost millions and false positives would damage customer relationships.

First iteration: We challenged the feature selection. AI had included obvious indicators but missed behavioral patterns from our domain knowledge – payment timing variations, seasonal cash flow patterns specific to the business sectors we serve. Accuracy jumped to 79%.

Second iteration: We adjusted how the model weighted base features (customer profile, income, loan amount) versus payment pattern features (repayment behavior over time, payment recentness, etc.). By teaching the model how much focus to give each type and analyzing their individual importance in predicting next month’s default, precision rose from 76% to 82%, and recall from 78% to 83%.

We also questioned the model’s treatment of missing data. AI had used standard imputation, but we knew from experience that in loan assessments, *why* data is missing often matters more than filling the gap. We refined the approach to treat missing data as a signal itself, which further improved both precision and recall.

Third iteration: AI suggested adding more historical data, but our real-world feedback showed the model was over-weighting pre-pandemic patterns. We adjusted the temporal weighting based on current economic conditions. Final model: 91% accuracy, 88% precision, and 89.5% F1 score.

The final system was something neither we nor AI would have reached independently. AI provided the computational power and pattern detection. We provided the domain expertise about what matters in loan risk assessment and why certain patterns emerge in our specific market context. Each iteration built on validated improvements – this is the feedback loop in action.