AI Thrives on Clear Context and Tight Constraints
Five Moves That Turn AI To Reliable Teammate
The Internet promised speed and leverage.
They promised your team could ship twice as fast with half the effort.
So now, senior leadership expects you to “figure out AI.”
But what no one says out loud is this:
AI only works when you’ve already done the work.
When your architecture is clear.
When your standards are explicit.
When your teams operate with clear context and constraints.
Today's AI Persona
AI is your newest teammate—knowledgeable across languages, relentlessly tactical, and eager to help. It writes code like a senior but makes design choices like a junior. Every task feels like its first day.
Yet even with its flaws, having such a partner for your work is life-changing.
Don’t ask whether AI can write good code. Ask which tasks are a good fit for AI, and how we can shape our work so more of them are?
Here are some hints:
1. Design for the AI’s First Day, Every Day
AI doesn’t carry memory between tasks unless you explicitly give it one.
It doesn’t know your architecture, your libraries, your team's flavor of clean code — unless you say it out loud.
Most misfires are onboarding gaps.
It’s like handing an intern a ticket with no background, no examples, no patterns, and then blaming them for being creative.
Make context explicit. Every time.
Try this
Prepend prompts with links to architecture.md, API docs, and example components. Don’t make it guess—give it the files and patterns to fit your requirements.
💡 Hint: if you don't have architecture.md, check this repo or generate one:
"I'm building //here goes description of your product or service//.
Here's my tech stack: Next.js, Prisma, Auth.js, Vercel.
Generate a markdown description of the current architecture, including project structure, key components, dependencies, and benefits of this architecture.
The desired architecture I want to follow is Clean Architecture".
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