AI Fundamentals· Lesson 9 of 10
- 1What Even Is AI? AI vs. ML vs. Deep Learning6 min
- 2How Models Learn: Training vs. Inference6 min
- 3What Is an LLM?8 min
- 4Tokens — the Currency of Everything7 min
- 5Why AI Output Is Probabilistic (and Why It Changes How You Build)7 min
- 6Hallucination — Why Models Make Things Up7 min
- 7What AI Is Genuinely Good and Bad At — a Capability Map7 min
- 8AI Systems vs. Traditional Software — the Mindset Shift7 min
- 9Where AI Fits in a Product (and Where It Shouldn't)7 min
- 10Responsible AI Basics for Builders7 min
Where AI Fits in a Product (and Where It Shouldn't)
In one line · AI shines on open-ended, repetitive language tasks; it's wrong when the task needs absolute precision or full explainability.
The biggest decision in any AI product is where to place it. The wrong spot burns budget and trust even with great execution.
Put AI where there's lots of language and little human patience: support, summarizing, triage. Keep it away from high-stakes decisions without a human in the loop.
Suggesting a support-ticket reply = great spot. Fully approving a loan with no human = wrong spot.
Apply this framework to any proposed feature: (1) Language volume — does the task involve lots of repetitive reading or writing? The more, the higher AI's value. (2) Cost of error — if a single mistake costs money, trust, or legal exposure, you need an approval gate: a human reviews before execution. (3) AI's role — let it suggest, draft, and triage; let humans or code decide and execute in sensitive spots. Golden rule: start with AI assisting a human, then expand its authority gradually as measured quality earns it.
🧪 Try it: in the Support Agent project, draw the ticket's journey from arrival to resolution, and mark on it: where does AI suggest? Where does a human approve (approval gate)? Where does ordinary code execute? Write a one-line reason for each choice.
Common mistakes: (1) adding AI for hype where ordinary code is better — a sort button needs no model; (2) giving AI final say on something sensitive from day one instead of starting it as a suggester; (3) measuring success by demo wow-factor instead of real time or money saved on repetitive tasks.
Golden spots: ticket triage and classification, reply suggestions, call and meeting summaries, turning free text into structured data, natural-language search over company docs.
Wrong spots: final decisions on money, health, or legal matters without a human; calculations that must be exactly right; anywhere you must precisely explain the decision to regulators; or features ordinary code solves cheaper and more reliably.