AI-Powered Development in 2026: How LLMs Changed My Daily Workflow

When I started shipping production apps a decade ago, "AI in the IDE" meant autocomplete that finished a variable name. In 2026, an AI assistant can scaffold an entire feature, refactor a module, and write the tests for it — sometimes in the time it would have taken me to read the ticket. As the founder of NORDEF, I rely on AI tools every day, and the way I write code has changed permanently.

This post is not a hype piece. It is a snapshot of what actually works, what does not, and the habits that make AI-assisted development sustainable rather than chaotic.

The tools I actually use

My stack is intentionally small. Adding more tools rarely makes you faster — it just adds context-switching cost.

• Lovable for spinning up full-stack web apps from a prompt, especially when I want a working baseline in minutes.
• Cursor for deep refactors and multi-file edits where I want tight control over diffs.
• GitHub Copilot for inline suggestions while I am in the flow.
• Claude / GPT in a separate window for architecture discussions, code review, and explaining unfamiliar libraries.

The new shape of a feature

A typical feature used to start with a blank file. Today it starts with a conversation. I describe the user-facing behavior, the data shape, and any constraints (auth, performance, edge cases). Then the assistant proposes a plan before touching any code. Reviewing that plan is now the most important step of my day — if the plan is wrong, the code will be wrong, and I will spend more time fixing it than if I had written it myself.

Prompting patterns that pay off

A few patterns consistently produce better output:

• Constrain the surface area. "Edit only this file" or "Do not change the public API" prevents collateral damage.
• Show, do not tell. Pasting a real example of the desired output (a JSON sample, a similar component) beats any amount of description.
• Demand a plan first. For anything bigger than a one-line change, I ask for a plan. If the plan is wrong, fixing the plan is cheap.
• Iterate in small steps. Long mega-prompts produce long mega-mistakes.

Where AI still loses

AI is excellent at boilerplate, translation between frameworks, and pattern matching against well-documented libraries. It is consistently weaker at:

• Domain logic that only lives in your team's head.
• Performance work where you have to reason about a real production trace.
• Security-sensitive code — RLS policies, auth flows, signature verification.
• Anything that requires sustained taste over many iterations.

On all of those, I treat AI output as a first draft, never a final answer.

A typical day

On a focused build day, I will move between three loops: planning with an AI chat, generating code in Cursor or Lovable, and reviewing the diff before committing. I never accept a diff I have not read. The discipline that used to go into writing code now goes into reading code — quickly, but completely.

# A prompt I reuse all the time
You are pairing with a senior engineer.
- Read the file before editing it.
- Propose a plan, wait for confirmation, then implement.
- Keep changes scoped to the requested behavior.
- Do not introduce new dependencies without asking.

The takeaway

AI did not replace the engineer; it raised the floor and the ceiling at the same time. Developers who keep their judgment sharp, read every diff, and stay curious about fundamentals are getting more leverage out of these tools than ever. Those who let the AI drive without supervision are shipping bugs faster than they used to ship features.

For me, the goal has not changed: ship reliable software that solves real problems. The tools just got a lot louder.