The demo works. The AI built it in an afternoon. Now you need it to handle real users, real data, and real edge cases. This is where most AI-built projects stall. The prototype feels like 80% of the work, but the remaining 20% is where things get difficult. If you want to scale an AI-built app, you need a different approach than the one that got you the first version.
Why prototypes hit a wall
AI app builders generate code fast. But the more code you generate, the more context the AI needs to understand your project. Eventually your codebase exceeds the model's context window, and the AI starts losing track of how things connect. It duplicates logic, overwrites working code, or misses dependencies between files.
This is not a flaw in the AI. It is a constraint of how large language models work. To scale an AI-built app past the prototype stage, you need to manage that constraint deliberately.
Manage context before it manages you
The first bottleneck you will hit is token limits. Every file the AI reads counts against its context window, and a growing codebase fills that window quickly.
Smart Context
If you are using Dyad, Smart Context addresses this directly. It uses a lightweight model to identify the most relevant files for each conversation and sends only those to the main model. This means the AI focuses on what matters instead of processing your entire codebase every turn.
Smart Context works well for targeted requests like "fix the login form" or "add a settings page." It struggles with broad prompts like "make this app better," where it cannot determine which files are relevant and falls back to including everything. For large projects, avoid vague prompts entirely.
Manual context management with glob paths
When your project grows past what Smart Context can handle, or when you need precise control, use Dyad's manual context management. You specify glob-style paths (e.g., src/components/**, lib/auth/*.ts) to tell the AI exactly which files to include.
This requires more setup, but it gives you reliable results on large codebases. You can also auto-include critical files like your database schema or shared types so the AI always has access to them, regardless of what else gets selected.
Keep in mind that limiting context means the AI will not see your full codebase. It might create duplicate code or write files in unexpected locations. Reviewing each change carefully matters more at this stage.
Split your app into modules
A single monolithic codebase is the hardest thing for an AI to work with at scale. As your project grows, splitting it into smaller, well-defined modules makes each piece easier to work on independently.
Some practical ways to split:
- Separate admin from user-facing features. If your app has an admin dashboard, it can often be its own project, deployed on a subdomain like
admin.yoursite.com. - Extract standalone pages. Marketing pages, landing pages, and documentation do not need to share a codebase with your core app.
- Group related features into directories. Even without splitting into separate projects, organizing code by feature (e.g.,
src/features/billing/,src/features/auth/) makes manual context management much more practical.
Modular structure also helps when you scale an AI-built app because each module can fit within a model's context window. You chat about the billing module with only the billing files loaded, and the AI produces better results than it would with your entire codebase in view.
Use AI_RULES.md to enforce production standards
During prototyping, the AI optimizes for speed. It picks whatever approach gets the feature working. For production, you need consistency and safety. Dyad's AI_RULES.md file lets you define rules the AI follows when generating code.
A critical example: production-safe database migrations. Add a rule like this to your AI_RULES.md:
When making any changes to the database schema, ensure all modifications are backwards compatible. New columns should have default values or be nullable. Do not remove or rename existing tables, columns, or constraints. Do not change data types in ways that break existing code.
This prevents the AI from generating a migration that drops a column your production users depend on. You can add similar rules for naming conventions, library choices, error handling patterns, or anything else you want enforced consistently.
The AI_RULES.md file is part of the system prompt, so the AI reads it before every response. Treat it as a living document. As you discover patterns that cause problems, add rules to prevent them.
Start new chats to keep conversations focused
Long chat histories degrade AI performance. As a conversation grows, the model has more context to juggle, and earlier instructions can get buried. When you notice the AI going in circles or producing inconsistent results, start a new chat.
In Dyad, all chats for a given app share the same codebase and version history. Starting a new chat gives you a clean context without losing any code. This is one of the most underused techniques for keeping AI output quality high as your project scales.
A practical rule: one feature per chat. If you are adding authentication, keep that in one conversation. When you move on to building the payment flow, start fresh.
Run a security review before shipping
AI-generated code tends to skip security basics. Hardcoded API keys, missing authentication checks, and overly permissive database access are common in prototypes. Before going to production, these need to be caught and fixed.
Dyad includes a built-in Security panel that scans your app for vulnerabilities using AI. Open it, click Run Security Review, and you get a table of findings sorted by severity (Critical, High, Medium, Low). Each issue has a Fix Issue button that opens a chat to address that specific vulnerability.
This is experimental and will not catch everything. I recommend pairing it with dedicated tools like Snyk or npm audit for dependency-level scanning. But Dyad's review catches the most common AI-generated security issues (hardcoded secrets, missing auth, open database policies) with minimal effort.
You can also create a SECURITY_RULES.md file to teach the security reviewer about your project's specific context, similar to how AI_RULES.md guides general code generation.
Version control as a safety net
Every change Dyad makes creates a new version (backed by git commits under the hood). If the AI breaks something while you are scaling your app, you can restore to any previous version from the version panel.
This matters more in production-bound projects than in prototypes. A bad change during prototyping is annoying. A bad change to production code can take down your app. Having automatic, always-additive version history means you can experiment aggressively and roll back instantly.
The production checklist
Before you deploy an AI-built app:
- Set up AI_RULES.md with production constraints (backwards-compatible database changes, consistent naming, error handling).
- Organize your codebase into feature-based directories or split into separate projects.
- Configure context management using glob paths for large projects.
- Run Dyad's Security Review and fix all Critical and High findings.
- Run a dependency scanner like Snyk or
npm audit. - Move all secrets to environment variables and confirm
.envis in.gitignore. - Test with real data and real user flows, not just the happy path.
Getting from prototype to production is the real work
The AI gets you a working prototype fast. Turning that into something reliable, secure, and maintainable is a different kind of problem. It requires managing context, enforcing standards, and reviewing what the AI generates with the same care you would give hand-written code.
Dyad is built for this transition. It is open-source, runs locally on your machine, and supports AI providers like OpenAI, Anthropic, and Google. The code it generates is standard React or Next.js with no vendor lock-in. You can scale an AI-built app in Dyad and take the result anywhere.
Download Dyad for free and start building.
