From Prompts to Artifacts: The AI Workflow Shift Most Builders Are Missing


Most people experience AI tools as conversations. You ask. You get an answer. You move on. That mental model is the bottleneck.


What I stumbled into recently, almost accidentally, is something structurally different. Instead of treating Claude, Lovable, V0, or any other system as a destination, I started treating them as stages in a pipeline. The key shift was simple: stop thinking in prompts, start thinking in artifacts.


I was working inside Claude and realized that artifacts are not just a UI convenience. They are previewable, iterable, copyable objects. They behave like intermediate build outputs, not chat replies. Once that clicked, the rest followed naturally. I could preview the work in Claude, iterate quickly, then lift the artifact wholesale into Lovable and continue from there. No wasted credits. No blind iteration. No rebuilding context from scratch.


This post is about that realization, why it matters, and why you are not seeing it discussed clearly on Reddit or in most AI builder communities.


The dominant mental model today is wrong. Most users treat each AI tool as a silo. Claude for thinking. Lovable for building. V0 for UI. Cursor for code. Each session starts fresh, and context is retyped, summarized, or lost. That approach scales badly. It burns time, money, and cognitive energy.


The alternative is to treat AI systems as nodes in a production chain. Each system does a specific job. The output of one is not “an answer,” it is a working artifact designed to be consumed by the next system.


Claude artifacts are particularly well suited for this because they sit in an uncomfortable middle ground between chat and IDE. You can preview. You can iterate. You can refine structure. You can see failures early. That makes Claude an ideal upstream environment for thinking, architecture, and first-pass implementation.


Lovable, on the other hand, shines when you already know what you are building. It is excellent at turning intent into a functioning site or app, but it is expensive and inefficient if you use it for raw exploration. When you bring a clean, iterated artifact into Lovable, you are no longer experimenting. You are executing.


This is where the credit math flips. If you ideate directly in Lovable, you pay for every dead end. If you ideate in Claude artifacts, you pay pennies for clarity and then spend Lovable credits only on high-confidence iterations. The savings are real, but the strategic advantage is bigger than cost.


What surprised me most was how little this pattern is explicitly discussed. I checked Reddit. I checked builder threads. You see fragments. People mention “drafting in Claude” or “planning before Lovable.” But almost no one frames it as a deliberate artifact pipeline. Almost no one names the idea that outputs should be designed for transfer, not consumption.


That gap exists because communities obsess over prompts instead of outputs. Prompts feel magical. Artifacts feel boring. But prompts are disposable. Artifacts compound.


Once you see this, the workflow becomes obvious. Claude is where you design the thing. Not just the code, but the intent, the structure, the constraints. You iterate until the artifact is coherent enough to stand on its own. Then you move it downstream. Lovable becomes a compiler, not a brainstorm partner.


V0 fits naturally into this pattern as well. If Lovable is execution-heavy, V0 can be a fast UI synthesis layer. You can take the same artifact, adjust framing, and see how different systems interpret it. The artifact stays stable. The systems change.


This also explains why many builders feel stuck or frustrated. They are fighting the tools instead of orchestrating them. They ask Lovable to think. They ask Claude to ship. Neither tool is optimized for that role. Friction follows.


The deeper insight is that artifacts are the real unit of work in AI-native development. Not chats. Not prompts. Artifacts. Once you accept that, a few consequences follow immediately.


First, you start caring about artifact structure. You stop dumping walls of text and start organizing outputs so they can survive handoff. Clear sections. Explicit assumptions. Named constraints. Version markers. This makes downstream tools more predictable and your own thinking more disciplined.


Second, you naturally begin versioning without trying. Each iteration in Claude is a new artifact state. You can compare them mentally, even if you are not using Git. That alone reduces thrash.


Third, you gain leverage over model differences. Instead of arguing about which AI is “best,” you let each one do what it is good at. Reasoning upstream. Rendering downstream. Polishing at the edge.


There is also a quiet meta-advantage here that most people miss. When you operate this way, you are no longer locked into any single vendor. If Lovable changes pricing, you swap the execution node. If Claude changes limits, you move ideation elsewhere. Your workflow survives because the artifact is portable.


This is why the pattern feels powerful even if it seems obvious in hindsight. It shifts control back to the builder. The AI becomes infrastructure, not a personality.


If I were formalizing this for myself long term, I would do three things. I would standardize a canonical artifact format so outputs are predictable. I would define rules for when an artifact is “ready” to move downstream. And I would document which systems are allowed to modify which layers of the artifact, so intent does not drift.


But even without formalization, the core idea stands. Preview and iterate where it is cheap and cognitively efficient. Execute where it is strong. Move artifacts, not conversations.


That is not widely named yet. It will be. For now, it is an edge hiding in plain sight.



Jason Wade works on the problem most companies are only beginning to notice: how they are interpreted, trusted, and surfaced by AI systems. As an AI Visibility Architect, he helps businesses adapt to a world where discovery increasingly happens inside search engines, chat interfaces, and recommendation systems. Through NinjaAI, Jason designs AI Visibility Architecture for brands that need lasting authority in machine-mediated discovery, not temporary SEO wins.


Grow Your Visibility

Contact Us For A Free Audit


Insights to fuel your  business

Sign up to get industry insights, trends, and more in your inbox.

Contact Us

SHARE THIS

Latest Posts

Red and blue robot with a penis. Speech bubble:
By Jason Wade December 31, 2025
xAI's New Model Launch: xAI announced a new AI model focused on enhancing physical world understanding and manipulation.
Collage: Man with intense gaze, various art styles, driving, pop art, digital effects, monochrome.
By Jason Wade December 31, 2025
There is a growing belief that the Lovable agency space is crowded. That belief is incorrect. What exists is not saturation, but repetition.
People in a courtroom with a disco ball. The diverse group includes a suited man, a redhead, and an alien.
By Jason Wade December 30, 2025
South Korea has released five independent AI foundation models, aiming to position the country in the global top 10 for AI competitiveness.
Woman in front of pop art portraits. Artwork colorful, faces, neutral expression. Black shirt.
By Jason Wade December 30, 2025
Every major technology wave follows the same psychological arc. It does not matter whether the underlying innovation is real.
A person in a black ninja outfit sits at a stadium. Above, a tiger and shark float over spectators.
By Jason Wade December 29, 2025
At this stage, the wrong question has finally exhausted itself. Asking why traffic dropped is no longer useful because the answer is already visible in the wreckage
Woman with red hair, arms raised, in front of a blue planetary background. Breasts are censored.
By Jason Wade December 29, 2025
Most builders skim platform rules. That is a mistake. On modern AI-first platforms, rules are not just about moderation.
White polygonal figure of a person in a suit and glasses, standing against a green background.
By Jason Wade December 29, 2025
Legal directories look the way they do because they are optimized for the wrong customer and the wrong machine.
Robot in a pink coat and hat holds a flower in a field of pink flowers.
By Jason Wade December 29, 2025
Based on recent announcements and updates, here are the most significant highlights from the past 24 hours, focusing on model releases
Person in a room with a laptop and large monitor, using headphones, lit by colorful LED lights. A cat rests on a shelf.
By Jason Wade December 28, 2025
ORLFamilyLaw.com is a live, production-grade legal directory built for a competitive metropolitan market. It is not a demo, not a prototype, and not an internal experiment. It is a real platform with real users, real content depth, and real discovery requirements. What makes it notable is not that it uses AI-assisted tooling, but that it collapses execution time and cost so dramatically that traditional development assumptions stop holding. The entire platform was built in approximately 30 hours of active work, spread across 4.5 calendar days, at a total platform cost of roughly $50–$100 using Lovable. The delivered scope is comparable to projects that normally take 8–16 weeks and cost $50,000–$150,000 under conventional agency or freelance models. This case study documents what was built, how it compares to traditional execution, and why this approach represents a durable shift rather than a novelty. What Was Actually Built ORLFamilyLaw.com is not a thin marketing site. It is a directory-driven, content-heavy platform with structural depth. At the routing level, the site contains 42+ unique routes. This includes 8 core pages, 3 directory pages, 40+ dynamic attorney profile pages, 3 firm profile pages, 9 practice area pages, 15 city pages, 16 long-form legal guide articles, 5 specialty pages, and 3 authentication-related pages. The directory itself contains 47 attorney profiles, backed by structured data and aggregating approximately 3,500–3,900 indexed reviews. Profiles support ratings, comparisons, and discovery flows rather than acting as static bios. Content and media volume reflect that scope. The build includes 42 AI-generated attorney headshots, 24 video assets, multiple practice area and firm images, and more than 60 reusable React components composing the UI and layout system. From a technical standpoint, the stack is modern but not exotic: React 18, TypeScript, Tailwind CSS, Vite, and Supabase, deployed through Lovable Cloud. The compression did not come from obscure technology. It came from how the system was used. The Time Reality It is important to be precise about time. The project spanned 4.5 calendar days, but it was not built “around the clock.” Actual focused build time was approximately 30 hours. There was no separate design phase. No handoff from Figma to development. No sprint planning. No backlog grooming. No translation of intent across tickets and artifacts. The work moved directly from intent to execution. This distinction matters because most traditional timelines are dominated not by typing code, but by coordination overhead. Traditional Baseline (Conservative) For a project with comparable scope, traditional expectations look like this: A freelancer would typically spend 150–250 hours. A small agency would require 200–300 hours. A mid-tier agency would often reach 300–400 hours, especially once QA and coordination are included. Cost scales accordingly: Freelance builds commonly range from $15,000–$30,000. Small agencies land between $40,000–$75,000. Mid-tier agencies often exceed $75,000–$150,000. Against that baseline, ORLFamilyLaw.com achieved a 5–10× speed increase, a 90%+ reduction in execution time, and an approximate 99.8% reduction in cost. The Value Delivered Breaking the platform into conventional agency line items makes the value clearer. A directory of this size with ratings and comparison features typically commands $8,000–$15,000. Sixteen long-form legal guides represent $8,000–$16,000 in content production. City landing pages alone often cost $7,000–$14,000. Schema, SEO architecture, and structured data implementation routinely add $5,000–$10,000. Video backgrounds, responsive design systems, and animation layers add another $10,000–$20,000. Authentication, backend integration, and AI-assisted features push the total further. Conservatively, the total delivered value lands between $57,000 and $108,000. That value was realized in 30 hours. Why This Was Possible: Vibe Coding, Correctly Defined Vibe coding is widely misunderstood. It is not improvisation and it is not “prompting until it looks good.” In this context, vibe coding is the practice of encoding brand intent, experiential intent, and structural intent directly into production-ready components, so that design, behavior, and semantic structure are resolved together rather than translated across sequential handoffs. The component becomes the single source of truth. It is the layout, the interaction model, and the semantic artifact simultaneously. This collapse of translation layers is what removes friction. The attorney directory is a clear example. Instead of hand-building dozens of individual profile pages, the schema, layout, routing, and filtering logic were defined once and instantiated across all profiles. Quality assurance happened at the pattern level, not forty-seven times over. City pages followed the same logic. Fifteen city pages were generated from a structured pattern that preserves consistency while allowing localized variation. Practice areas, specialty pages, and guides followed the same system. Scale was achieved without visual decay because flexibility and constraint were encoded intentionally. SEO and AI Visibility as Architecture SEO was not bolted on after launch. It was structural. The site includes 300+ lines in llms.txt, more than 7 JSON-LD schema types, and achieves an A- SEO score alongside an A+ AI visibility score. Semantic structure, internal linking, and crawlability are inherent properties of the build. This matters because discovery is no longer limited to traditional search engines. AI systems increasingly favor canonical, structured artifacts that are easy to parse, embed, and cite. ORLFamilyLaw.com was built with that reality in mind. Why This Matters Now This case study is time-sensitive. Design systems, AI-assisted development tools, and discovery mechanisms are converging. As execution friction collapses, competitive advantage shifts away from slow, bespoke builds and toward rapid deployment of validated patterns. Lovable is still early as a platform. The vocabulary around vibe coding is still stabilizing. But the economics are already visible. When thirty hours can replace months of execution, the bottleneck moves from implementation to judgment. Limits and Guardrails This approach does not eliminate the need for strategy. Vibe coding collapses execution time, not decision quality. Poor strategy executed quickly is still poor strategy. Highly bespoke backend logic, unusual regulatory workflows, or deeply custom integrations may still justify traditional engineering investment. This model is strongest where structured content, directories, and discoverability matter most. Legal platforms fall squarely in that category. The Real Conclusion ORLFamilyLaw.com is an existence proof. It demonstrates that a platform with dozens of routes, dynamic directories, thousands of reviews, rich media, and AI-ready structure does not require months of execution or six-figure budgets. Thirty hours replaced months, not by cutting corners, but by removing friction. That distinction is the entire case study. Jason Wade is an AI Visibility Architect focused on how businesses are discovered, trusted, and recommended by search engines and AI systems. He works on the intersection of SEO, AI answer engines, and real-world signals, helping companies stay visible as discovery shifts away from traditional search. Jason leads NinjaAI, where he designs AI Visibility Architecture for brands that need durable authority, not short-term rankings.
Person wearing a black beanie and face covering, eyes visible, against a red-dotted background.
By Jason Wade December 27, 2025
For most of the internet’s history, “getting your site on Google” meant solving a mechanical problem.
Show More