We Connected an LLM to a 12-Year-Old Codebase. Here's What Broke.
Integrating a large language model (LLM) into an existing 12-year-old fintech codebase presented several challenges. Initial attempts led to significant issues, including application submission delays and incorrect risk scoring due to poor data quality. Ultimately, the team implemented a gateway to manage LLM interactions, improving reliability and performance.
- ▪The integration of an LLM into a legacy codebase caused significant operational issues.
- ▪Initial integration attempts resulted in application submission delays when the LLM response times increased.
- ▪Data quality issues led to incorrect risk scores, highlighting the importance of data auditing in AI projects.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3880099) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Empiric Infotech LLP Posted on May 21 We Connected an LLM to a 12-Year-Old Codebase. Here's What Broke. #ai #architecture #llm #node Every "add AI to your product" tutorial assumes you are starting fresh. Greenfield repo, clean data, no users yet. Real integration work looks nothing like that. Last year our team picked up a fintech client with a loan-application platform that had been running since 2014.
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