- Miron from Vibe Code Lab
- Posts
- how to prompt like a pro (or at least not like a bot)
how to prompt like a pro (or at least not like a bot)


alright.
this week’s drop is about prompt engineering — but without the fluff.
no “ultimate 500 prompt pack.”
just raw strategy, chaos-tested workflows, and what actually works when you’re solo-building with AI.
1. Prompting is interface design
a prompt isn’t a wish. it’s a wireframe in words.
bad: build me a dashboard with login, stats, and user settings
better: you’re my react assistant. we’re building a dashboard in next.js. start with just the sidebar. use shadcn/ui
. don’t write the whole file. I’ll ask for parts one by one.
scope small. give context. assign roles.
don’t write essays. don’t vibe your way into confusion.
2. Waterfall prompting = guided discovery
some of the best prompts? not prompts. they’re mini-interviews.
example:
q1: what is y combinator?
q2: do they publish startup lists?
q3: what’s the best way to find all W23 startups?
q4: what trends show up across batches?
q5: if I wanted to build a local clone of the best idea from S22, what would that look like?
you just walked ChatGPT from “what is YC” to “help me build a startup idea from YC trends.”
same thing applies to coding:
q1: what file touches this component?
q2: what logic is shared across it?
q3: if I wanted to add a feature without breaking anything else, what’s the cleanest way?
q4: okay, let’s implement your idea — scoped and testable.
prompt like you’re talking to a junior dev. walk them there.
Stop 3 am bug-hunts → ship fixes in 30 min

Debug & Implement guide cover
most devs burn out post-MVP
this guide helps you debug smart, refactor clean, and implement without fear
✓ scoped debug rituals
✓ implementation prompt flows
✓ how to not let AI nuke your repo
3. Treat AI like a team
I don’t use GPT-4 for UX wireframes. I don’t ask Claude to debug SQL.
every model has a role.
craft many chats within one project inside your LLM for:
→ planning, analysis, summarization
→ logic, iterative writing, heavy workflows
→ scoped edits, file-specific ops, PRs
→ layout, flow diagrams, structural review
you wouldn’t ask your UI designer to rewrite your backend. same rule applies here.
4. Short chats. always.
one feature = one chat.
one bug = one thread.
one idea = one prompt.
don’t open 300-line monologues and expect quality.
context drifts. hallucinations spike.
AI becomes a toddler with scissors.
5. Prompt iteratively (not magically)
inspired by Matt McCartney’s blog (worth the read here): LLMs aren’t search engines. they’re pattern generators.
so give them better patterns:
set constraints
define the goal
include examples
prompt step-by-step
the best prompt is often... the third one you write.
6. Save your best prompts like code
your prompt library is as important as your repo.
I version mine like this:
feature_implementation_prompts.md
debugging_routines.md
prompt_snippets_ui_gen.md
build once, reuse everywhere.
save your gold.
7. My personal stack right now
what I use most:
ChatGPT with Custom Instructions for writing and systems thinking
Claude / Gemini for implementation and iteration
Cursor + BugBot for inline edits
Perplexity Labs for product research
also: I write most of my prompts like I’m in a DM with a dev friend.
it helps.
8. Debug your own prompts
if AI gives you trash, it’s probably your fault.
go back and ask:
did I give it a role?
did I share context or just vibes?
did I ask for one thing or five?
did I tell it what not to do?
90% of my “bad” AI sessions came from lazy prompts, not dumb models.
Seen this week (stuff worth knowing)
some bits that hit my radar this week — signal only:
Grok just launched Tasks — lets you track news, competitors, and complex topics on schedule. finally starting to feel like a real assistant.
this Google engineer dropped a killer prompt playbook for devs → the prompt engineering guide
Sam Altman on OpenAI’s new podcast:
GPT-5 → coming this summer
debates over naming clarity (they’re worried we’re confused — we are)
Midjourney v1 = video gen is here. think: prompts → motion.
Google Search Live Mode now lets you speak in AI Mode.
MIT brain scan study:
ChatGPT users forgot what they wrote 5 minutes later
neural connections dropped from 79 → 42
(the AI is smart. your brain? might get lazy.)
more coming next week.
until then:
stay caffeinated.
lead the machine.
launch anyway.
☀️ miron