For twenty years, we built software products following two main approaches. Waterfall: plan everything upfront, build step by step. Predictable, but rigid the moment requirements change. Then agile: short iterations, continuous delivery, learning along the way.
With generative AI, a third paradigm is emerging. And it looks nothing like the first two. Welcome to the era of vibe coding.
What is vibe coding, concretely?
The term "vibe coding" was popularized by Andrej Karpathy, OpenAI co-founder, to describe a new way of programming: you describe what you want in natural language, and the AI generates the code. The developer no longer codes line by line — they steer the generation, iterate on results, adjust direction.
But vibe coding is far more than a programming technique. It is a new mental model for product creation.
Traditionally, we started from a user need, decomposed it into specifications, then built. With AI, the process partially inverts: you start from an idea, explore what the model can do, then shape a product from that exploration. The first version is often strange — you aimed for a car, you got a cat, then a duck. But that "weird" first version is what reveals the model's real potential.
In my engagements, I see this dynamic repeat itself: the teams that succeed with AI are not the ones who specify everything upfront. They are the ones who know how to explore, pivot, and shape from what the AI produces.
The three paradigms of product building
To understand where we are, we need to visualize the evolution:
Waterfall (1970s-2000s): complete upfront planning, sequential execution. The final product resembles the initial plan. Value arrives at the end. So does risk.
Agile (2000s-2020s): short iterations, continuous feedback. You start with a skateboard and end up with a car, passing through a bicycle and a motorcycle. Henrik Kniberg's metaphor is famous — and accurate. Value arrives early. Pivoting is possible.
Vibe coding (2024+): AI-guided exploration. You start with a vague intention, generate prototypes in minutes, discover possibilities you hadn't imagined. The product emerges from human-AI interaction, not from a predetermined plan. Value arrives immediately. The outcome is often unexpected.
This third paradigm doesn't replace the first two. It complements them. But it demands a radical change in posture.
RAG and CAG: the two engines of contextual AI
For vibe coding to work in enterprise settings, AI can't rely on generic knowledge alone. It needs to understand your context. Two strategies drive this contextualization:
RAG (Retrieval-Augmented Generation) — like a brain that reads a file before answering. The AI queries your document bases, emails, wikis, and tickets in real time to stay current. Ideal for fast-moving environments: sales, customer support, competitive intelligence, proposal responses.
CAG (Cache-Augmented Generation) — like a brain that has already memorized the right reflexes. You teach it once, it responds instantly. Perfect for stable contexts: internal processes, FAQs, business procedures, compliance rules.
The difference is crucial. RAG is dynamic but slower (reads at each request). CAG is fast but requires prior training. In practice, the best implementations combine both: CAG for the stable knowledge base, RAG for fresh data.
RAG and CAG are the bridge between your organization's collective intelligence and artificial intelligence. And that is what truly augmented decision-making looks like.
What this changes for product teams
Vibe coding reshuffles roles within teams. The Product Owner no longer writes detailed user stories before the sprint. They explore with AI in real time, generate functional prototypes in minutes, and adjust the product on the fly.
The developer no longer types every line of code. They orchestrate generation, validate architecture, secure outputs. Their role shifts from production to curation.
The designer no longer delivers static mockups. They create interactive prototypes instantly and test with users in real time.
On the ground, teams that adopt this paradigm see three measurable changes: fewer specification errors (they test instead of guessing), faster decisions (the prototype replaces the theoretical debate), and an AI that truly understands the business (thanks to RAG/CAG).
The traps to avoid
Vibe coding is not a silver bullet. I see three recurring mistakes in organizations.
Confusing speed with quality: generating code fast doesn't mean generating good code. Without review, testing, or architecture, vibe coding produces technical debt at unprecedented speed. AI amplifies everything — productivity and chaos alike.
Believing AI replaces product thinking: AI generates solutions, not well-formulated problems. The ability to identify the right problem to solve remains fundamentally human.
Ignoring governance: who decides when AI output is "good enough"? Who is accountable for the quality of generated code? Without clear answers, vibe coding becomes a risk factor.
How to integrate vibe coding in your organization
My field approach follows three phases. First, an AI maturity diagnostic for your product teams: what tools are they already using? Which workflows are candidates for vibe coding? Where are the risks?
Then, a pilot on a concrete use case: we pick a feature, build it in vibe coding mode with proper guardrails (RAG for business context, code review, automated tests), and measure results.
Finally, a scaling framework: how to move from a pilot to a team practice, with the right governance, quality, and training guidelines.
Vibe coding is not a trend. It is the third chapter of product building. After the plan (waterfall) and the iteration (agile), here comes exploration (AI). The organizations that learn to navigate between these three paradigms will be the ones building the best products.