I built an AI resume tool and it has taught me lessons on how to overcome 5 biggest changes with "Wrapper" AI app. The AI resume tool "papercut.cv" can analyze GitHub repositories and creates tailored resumes based on job descriptions. This project combines multiple cutting-edge technologies including Go backend services, Next.js frontend.
At first, I thought it was just an easy wrapper AI app—one that would work simply by integrating a few APIs. But as I delved deeper into development, I realized there are significant challenges to making the app run sustainably and keep costs in check.
This blog post explores the key technical challenges I encountered and the innovative solutions I implemented to create a robust, scalable, and user-friendly AI resume generation platform.
Challenge 1: Large Repository Analysis Timeouts
The Problem
Large repositories could take over 20 minutes to analyze, causing cascading timeouts across the system. And users would lose progress just when their resume was almost ready.
The Solution
I used Map-Reduce technology to handle big repos, to break big repos into chunks, and implemented adaptive timeout management that adjusts based on repository size. An exponential backoff retry mechanism prevents immediate failures, and resilient EventSource connections automatically reconnect to preserve user progress.
Challenge 2: Multi-Model AI Integration
The Problem
Each AI provider uses different streaming response formats - Claude uses content_block_delta, OpenAI uses choices[].delta.content, and Gemini has its own protocol. This led to parsing failures.
The Solution
I built a universal streaming parser with intelligent format detection that automatically identifies the AI provider and applies the correct parsing strategy.
Challenge 3: AI Token Management and Cost Optimization
The Problem
Processing large repositories consumed massive amounts of AI tokens, leading to unpredictable costs and potential rate limiting. A single large repository analysis could cost $50+ in API calls, making the service economically unsustainable.
The Solution
I developed intelligent token estimation and budget management. The system pre-calculates token usage before processing, implements dynamic chunking strategies to stay within budget limits.
Challenge 4: Real-time AI Streaming with Error Recovery
The Problem
AI streaming responses would frequently break mid-generation, leaving users with incomplete resumes. Network hiccups, model timeouts, or malformed JSON would cause the entire generation process to fail, wasting both time and API costs.
The Solution
I built a resilient streaming architecture with checkpoint-based recovery. The system saves intermediate states, can resume from the last valid checkpoint, and implements intelligent retry logic that doesn't restart the entire process when only the final stage fails.
Challenge 5: AI Model Fallback and Reliability
The Problem
AI services are inherently unreliable - models go down, rate limits are hit, and API keys get exhausted. Relying on a single AI provider meant that service outages would completely break our application.
The Solution
I implemented a sophisticated fallback system that automatically switches between AI providers when one fails. The system maintains quality standards across different models by adjusting prompts and post-processing logic based on each model's strengths and weaknesses.
A small tip: You can use the SMTP(smtp.gmail.com) to send warning emails to yourself when server is broken or anything wrong, make you to fix before users remind you, to guarantee a reliable system.
Key Insights for AI Application Development
Cost Management is Critical
AI applications face unique economic challenges. Token costs can spiral out of control quickly, making cost optimization a first-class engineering concern. Successful AI applications require sophisticated budget management and intelligent model selection strategies.
AI Reliability Requires Redundancy
Unlike traditional web services, AI providers are inherently unreliable. Building production-ready AI applications means planning for model failures, API outages, and inconsistent outputs from day one. Fallback systems aren't optional - they're essential.
Prompt Engineering is Software Engineering
Managing AI prompts requires the same discipline as managing code. Version control, testing, and systematic optimization of prompts are crucial for maintaining consistent quality as your application scales.
Streaming Architecture for AI
Real-time AI applications demand robust streaming architectures. Users expect immediate feedback, but AI processing takes time. Building systems that can gracefully handle interruptions and resume processing is essential for good user experience.
Quality Control at Scale
AI outputs are unpredictable by nature. Production AI applications need automated quality scoring, validation systems, and retry mechanisms to ensure consistent results without manual intervention.
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