Ten Lessons From Gen AI Systems That Actually Shipped
Field notes from real deployments across healthcare, logistics, retail, fintech and martech. No theory, no vendor slides. Just what held up in production and what did not.
Ten Lessons at a Glance
Written for technology leaders, product managers and decision-makers who need Gen AI systems that are reliable and cost-effective, not just impressive in a demo.
Use-Case Selection
Most Gen AI failures happen before the first prompt is written. Pick problems where an 80% right answer still creates value.
Human-in-the-Loop
Design review into the workflow from day one. Bolting it on after the first bad output is twice the work.
New UX Paradigms
A chat box is not a strategy. The best AI interfaces make the model invisible and the outcome obvious.
ML Is Not Dead
Classic models still win on cost, latency and accuracy for structured problems. The right answer is usually both.
Synthetic Data
When real data is scarce or sensitive, generated data can bootstrap a system. Only if you validate it relentlessly.
Small Models, Agentic Patterns
A fleet of small, focused models often beats one giant one on cost, speed and reliability.
Beyond Chat
Extraction, summarization and classification are the quiet workhorses. They ship value without a chat window.
Limit LLM Decisions
Let the model draft, never let it decide. Deterministic logic stays in charge of anything that matters.
QA for AI
You cannot unit-test a probability. Build evals, regression suites and drift monitoring instead.
Latency-Aware Design
Users forgive a wrong answer faster than a slow one. Architect for perceived speed from the start.
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