Built a ai wrapper / llm app with Fine.dev?
We'll make it production-ready.
AI wrapper apps — products built on top of OpenAI, Anthropic, or other LLM APIs — have unique production challenges. The AI model is a black box that's slow, expensive, and unpredictable. Your app needs to handle variable response times, API failures, cost management, and output quality control in ways that standard web apps don't.
AI Wrapper / LLM App challenges in Fine.dev apps
Building a ai wrapper / llm app with Fine.dev is a great start — but these challenges need attention before launch.
API cost management
LLM API calls are expensive. A single unoptimized prompt can cost cents per request — which adds up fast with real users. You need token counting, cost tracking, usage limits per user, and prompt optimization to stay profitable.
Response time variability
LLM responses take 1-30 seconds depending on prompt complexity, model load, and output length. Your UI needs streaming responses, loading states, and timeout handling. Users abandon apps that feel slow.
Error handling for AI responses
The AI model might: return an error, time out, return malformed output, refuse to answer, or hallucinate. Each case needs specific handling. AI tools build the happy path but not the many failure modes.
Prompt injection and security
Users can manipulate your AI's behavior through carefully crafted inputs — making it ignore instructions, reveal system prompts, or produce harmful output. Input sanitization and output validation are essential.
Rate limiting and queuing
LLM APIs have rate limits. When many users make requests simultaneously, you need a queue system to manage the flow and provide feedback to waiting users. Without this, users get API errors during peak usage.
Output quality control
LLM responses aren't deterministic — the same prompt can produce different quality results. You need output validation, retry logic for poor responses, and potentially human review for critical outputs.
What we check in your Fine.dev ai wrapper / llm app
Common Fine.dev issues we fix
Beyond ai wrapper / llm app-specific issues, these are Fine.dev patterns we commonly fix.
Spec interpretation may differ significantly from intent
Fine.dev builds exactly what the specification says, not what you meant. Ambiguous specs lead to technically correct implementations that miss the actual product requirement.
Cross-file changes may miss security implications
When Fine.dev generates features spanning multiple files, security-sensitive changes — new endpoints, auth checks, data access patterns — are spread across the PR without a consolidated security review surface.
Generated tests may not cover real-world edge cases
Fine.dev writes tests for the happy path and obvious failure cases from the spec. Edge cases not explicitly mentioned — concurrent requests, null inputs, rate limits — are frequently untested.
Cross-file changes introduce inconsistencies
Large implementations touching many files accumulate subtle inconsistencies — type assumptions that don't hold across modules, different error handling patterns in different files.
Start with a self-serve audit
Get a professional review of your Fine.dev ai wrapper / llm app at a fixed price.
External Security Scan
Black-box review of your public-facing app. No code access needed.
- OWASP Top 10 vulnerability check
- SSL/TLS configuration analysis
- Security header assessment
- Expert review within 24h
Code Audit
In-depth review of your source code for security, quality, and best practices.
- Security vulnerability analysis
- Code quality review
- Dependency audit
- Architecture review
- Expert + AI code analysis
Complete Bundle
Both scans in one package with cross-referenced findings.
- Everything in both products
- Cross-referenced findings
- Unified action plan
100% credited toward any paid service. Start with an audit, then let us fix what we find.
Frequently asked questions
Can I build a ai wrapper / llm app with Fine.dev?
Fine.dev is a great starting point for a ai wrapper / llm app. It handles the initial scaffolding well, but ai wrapper / llm apps have specific requirements — api cost management and response time variability — that need professional attention before launch.
What issues does Fine.dev leave in ai wrapper / llm apps?
Common issues include: spec interpretation may differ significantly from intent, cross-file changes may miss security implications, generated tests may not cover real-world edge cases. For a ai wrapper / llm app specifically, these issues are compounded by the need for api cost management.
How do I make my Fine.dev ai wrapper / llm app production-ready?
Start with our code audit ($19) to get a clear picture of what needs fixing. For most Fine.dev-built ai wrapper / llm apps, the critical path is: security review, then fixing core flow reliability, then deployment. We provide a fixed quote after the audit.
How much does it cost to fix a Fine.dev-built ai wrapper / llm app?
Our code audit is $19 and gives you a complete report of issues. Fixes start at $199 with our Fix & Ship plan. For larger ai wrapper / llm app projects, we provide a custom fixed quote after the audit — no hourly billing.
Get your Fine.dev ai wrapper / llm app production-ready
Tell us about your project. We'll respond within 24 hours with a clear plan and fixed quote.