From vibe to production
Vibe coding gets you a prototype in minutes. Production still takes engineering.
11 chapters, from the first prompt to an agent
Specs and context, tests, security, evals, RAG, cost, data and agents with brakes.
Free. It costs an email.
The full PDF — 21 illustrations, checklists and an exercise in every chapter.
It demos beautifully
You describe the idea, AI returns a working app in minutes. It feels like magic, it looks like a product, and the demo runs clean. But the demo is the happy path: one user, tidy data, a stable network and no attacker. Then real users show up. Production is everything else. A hundred people at once. Empty fields, accents and emojis. Timeouts, a dead API, a payment declined at the worst possible moment. And someone probing exactly the thing you skipped. AI didn't make good engineering optional — it just made bad engineering faster. This e-book is the map of the boring part: the part that decides whether your prototype survives its own success.
The three parts
Part I · Building with AI
AI as a developer tool. From vibe to production, specs and context, tests and the definition of done, and the security of generated code. Chapters 1 to 4.
Part II · AI products in production
AI as a product component. Evals and model upgrades, designing for failure, RAG and the customization ladder, cost and latency, data and observability. Chapters 5 to 9.
Part III · Agents
Where the two meet: an agent is both code you write and a system that makes its own decisions. Brakes, memory and multi-agent orchestration. Chapters 10 and 11.
The 11 chapters
01 · From vibe to production
Vibe coding gets you a prototype in minutes; production still takes engineering, and AI only changed how fast you run up that bill or pay it down.
02 · Specs, context and prompts
The quality of what AI returns depends less on a clever phrase and more on what you decided beforehand (the spec) and what you show it (the context).
03 · Tests, review and the definition of done
Untested AI code is a guess delivered with a confident accent. Lock behavior down with tests, have a second AI attack the result, and let only a human call it done.
04 · Security: from generated code to attacked feature
Trust the speed, verify the result. New risks in the code it writes (invented packages, exposed secrets) and in the feature you ship (prompt injection).
05 · Measuring quality: evals and golden datasets
"It seemed better when I tried it" is not a metric. If you can't measure an AI feature, you're not shipping, you're betting. And every model swap is a deploy.
06 · Designing for failure
Hallucination isn't a bug you'll fix someday; it's the default behavior. Design the product to stay honest when the model isn't.
07 · RAG and the customization ladder
RAG isn't magic; it's a search engine with a language model stapled on top. It's only as good as the worst passage it retrieves, and its ceiling is set upstream, at chunking.
08 · Cost and latency
Every token has a price and every second of waiting costs trust. The demo hides both bills. And the model API is the cheapest line in the AI budget.
09 · Data, privacy and observability
Your prompt isn't private; it's another company's log file. Draw the data boundary before the first call, and make every call observable without turning monitoring into a leak.
10 · Agents with brakes and memory
An agent without limits is a bill without a ceiling. Brakes aren't the opposite of autonomy; they're what makes it safe enough to ship.
11 · Multi-agent orchestration
"Multi-agent" isn't an architecture or an upgrade; it's a set of trade-offs. Teams of agents win on breadth and lose on depth.
Who it's for
For people who already use AI to write code, or who have already shipped an AI feature, and want it to hold up under real users. Developers, tech leads and technical founders. It requires no math and no machine learning experience. You can read the chapters in any order — but Chapter 5 (evals) and Chapter 8 (cost) are the ones the others keep referring back to. If you're short on time, read those two first.
Who wrote it
A boutique software and AI studio
Kuaray Tech builds custom software and AI products for people who need them to hold up in the real world. Small teams, end-to-end ownership, no reseller layer.
Written from what's actually running
Every chapter comes out of a decision made in production — evals that blocked a model upgrade, cost math redone, agents fitted with brakes. It isn't a paper summary.
Numbers with an expiry date
Model prices, library versions and benchmarks were checked between Oct 1 and Oct 13, 2026. The book says so plainly: redo the math with current figures.
Vibe the idea. We'll build the thing that survives success. Vibe coding gets you to a prototype. Getting to production — something secure, scalable and solid enough to build a business on — still takes people who know what they're doing. That's the boring part. It's also the part that matters, and it's exactly what we do.
FAQ
Is the e-book really free?
It is. It costs an email: fill in the form and the full PDF unlocks immediately — no charge, no trial.
Do I need to know machine learning?
No. It's written for people who build software, not for people who train models. There's no math, and the concepts that do come up are explained in the glossary in Appendix B.
Is it useful if I only use AI to code and don't build AI products?
Yes — Part I is entirely about that: specs, context, tests, review and the security of AI-written code. Parts II and III are waiting for when your first AI feature lands on the roadmap.
What's the format and size?
A PDF of roughly 11 MB, 83 pages, with 21 illustrations and charts. It reads well on screen and prints decently.
What happens to my email?
It's used to unlock the material and for follow-up related to it. Getting future content is a separate checkbox you tick if you want it. Request deletion any time at info+privacy@kuaray.tech.
Is there a Portuguese edition?
There is: Do Vibe à Produção, same content, at oka.kuaray.tech/p/oka/do-vibe-a-producao.