I'm going to be honest with you.
Most engineers using AI assistants today are shipping at the same speed as before.
They have Cursor. They have Claude Code. They watched the demos, saw the vibe coding videos, and said, "This is going to change everything." And nothing changed.
Here's the thing nobody wants to say out loud: LLMs are amplifiers. Not magic.
That's it. That's the whole thesis. An amplifier takes whatever signal you give it and makes it louder. If you plug in a clean signal, you get a clean sound. If you plug in noise, you get louder noise.
Most engineers are plugging in noise.
They open a chat window with zero context. No specs. No planning. No clear definition of what they're building. They ask the model to write a feature; it generates something; they copy it; it breaks; they debug for two hours; they paste the error back in; it generates a fix that breaks something else. And they call that AI-assisted development.
That's not development. That's chaos at 10x speed.
Here's what's really happening in this AI era. You're no longer a developer. You're the product manager, the architect, the QA engineer, AND the developer, all at once.
But if you don't know the principles of software projects, you're just equipped with a tool that mirrors your disorganization back at you confidently and at scale.
You didn't get slower because AI is bad. You got slower because you skipped the step that makes AI powerful: the spec.
Now, let me be clear — you're not alone in this. I'm not saying you're incompetent.
Some of the best engineers I've seen fall into this trap. And there are workflows out there built specifically to solve it.
BMAD. Agent Skills. Multiple frameworks built by engineers with real experience, engineers who lead real projects
They're not geniuses
Just people who mastered the principles of project management before they touched a model.
These workflows exist because the people who built them understood one thing: you don't pick up tools before you have a plan.
I want to share my version. Four phases that are similar to the well-known Spec-Driven Development with LLMs.
Every artifact across all four phases is stored in a Markdown file. That's not an accident — markdown keeps your workflow portable, readable, and model-agnostic. Whatever model is trending next month, your specs still work.
Phase 1 — Context
This is where you feed the model everything before it writes a single line of code.
Epics. User stories. Story descriptions. Technical requirements. Stack decisions. Non-negotiables.
Think of it as onboarding a new engineer to your team. You would never hand someone a laptop on day one and say, "Build the app." You walk them through the product. You explain the architecture. You tell them what they cannot touch. You set the context.
Do the same thing with your LLM.
No context = the model is going to hallucinate. And I've seen this happen. Engineers waste hours debugging code that was never wrong — it just solved a different problem than the one they had in their head.
Phase 2 — Planning
Now the model has context. Time to make a plan.
You instruct the model to break each user story into small, achievable milestones. Not one big chunk. Not a wall of code. A sequenced plan with clear checkpoints. And you save that plan to a markdown file.
This step alone separates engineers who are faster with AI from engineers who aren't.
Why? Because planning forces clarity. It forces you to think five steps ahead instead of just reacting to whatever the model generates.
You know what the difference between an engineer who stagnates and one who evolves is? One of them attacks their work with intention. The other just reacts.
The planning phase is where you build intention into your workflow.
Phase 3 — Building
This is where the actual development happens. And there is one rule. One non-negotiable.
The model does not move to the next step without your approval.
Every milestone. You review it before the model continues.
I know what you're thinking — that sounds slow. It's not. It's the opposite. Because the engineers who skip this step are the ones ending up with 3,000 lines of AI-generated code they don't understand and cannot debug. Then they spend three days trying to reverse engineer what the model was built for. That's not fast. That's a disaster.
Approval gates keep you in the loop. And in this phase, you can also bring in a second model as a code reviewer. You can use a skill to generate PRs. You can run automated checks.
The workflow becomes a system, and a system is what separates someone who scales from someone who grinds.
Phase 4 — Learning
This is the phase almost nobody talks about. And it's the most powerful one.
When you encounter a bug, for example, and you fix it, the model should update its memory — its instructions, its specs — so that pattern never happens again. If you find a fix, the model documents it. If you discover a constraint specific to your stack, it gets added to the context for the next session.
Phase 4 turns your AI assistant from a stateless code generator into something that actually learns how you work. Your conventions. Your edge cases, etc.
Most engineers use AI like a calculator. They pick it up, punch in a problem, get an answer, and put it down. They don't build on it. They don't make their codebase smarter over time.
That's why their shipping speed hasn't changed. The tool never learned them.
There are many AI workflows available right now — BMAD, Agent Skills, and others — built by engineers who've shipped real products. They're worth studying. Use them.
But before you pick one, understand the four principles underneath all of them:
Context → Planning → Building with approval gates → Learning.
Master those, and it won't matter which model is trending, which tool drops next week, or what the next big demo on X looks like.
LLMs are amplifiers. Feed them a solid spec — they'll amplify your output. Feed them chaos — and they'll amplify that too.
The choice is yours.
I break down engineering workflows, career strategy, and how to level up in tech on the TecPatri podcast. If you want to go deeper, the link is here: https://www.youtube.com/@TecPatri