Introduction: The more AI makes things easier, the wider the gap among engineers
Generative AI (LLMs) has become commonplace, and coding has accelerated. But at the same time, it has become a situation where anyone can produce something that looks right, and the value gap tends to appear in places other than the implementation itself.
This article distills the essential skills to become a strong engineer in the AI era into 10 items, framed so that you can imagine what to do tomorrow. We'll cover difficult topics too, but we'll keep it approachable.
10 Essential Skills: From Being a User of AI to Making AI Your Weapon
1. Problem Framing (Articulating what to solve)
AI can generate many candidate solutions. But what to solve is vague, and you’ll quickly lose your way.
- Goal: Revenue? Cost? User experience? Risk reduction?
- Constraints: deadlines, budgets, legal, existing systems, operations
- Success Metrics: SLO/SLI, CTR, churn, effort reduction, etc.
In practice, when you hear that AI could be used to do something, the strong engineer is the one who immediately asks, “and what would need to get better, and by how much, for it to be considered a success?”
2. Requirements Definition and Specification (Turning vague wishes into implementable specs)
Generative AI can create convincing text or code, but when requirements are vague, the output quality becomes vague. The strength of engineers is in drawing the boundary of the specification.
- User stories (e.g., who, for what purpose, what to do)
- Acceptance criteria (Given/When/Then)
- Non-functional requirements (performance, availability, audit logs, permissions)
Even when consulting with AI, having well-defined specifications increases the chance of a good output.
3. AI Literacy (Understanding the strengths and weaknesses of LLMs)
“If AI says it, it must be correct” is dangerous. Strong engineers treat LLMs as a convenient reasoning engine while also understanding their limits.
- Hallucination: can confidently tell a lie
- Data freshness: assumes it does not know the latest specs or internal circumstances
- Probabilistic outputs: same question yields different answers
Conversely, its strengths include summarization, translation, ideation, scaffolding code, and test-case ideas. If you can design where to rely on AI, and where humans verify, you’re a notch higher.
