RAG vs Fine-Tuning: What I Actually Learned After 6 Months of Building LLM Apps
Six months ago my team was building an internal support tool for a B2B SaaS company β about 120 employees, docs spread across Notion, Confluence, and a hal
Six months ago my team was building an internal support tool for a B2B SaaS company β about 120 employees, docs spread across Notion, Confluence, and a hal
Three weeks ago I was debugging a race condition in our WebSocket event handler β the kind of bug that only shows up under load, with no clean repro steps.
Six months ago my team shipped a customer support bot that confidently told users our return window was 60 days. Itβs 30.
Three weeks ago I got annoyed.
Last November, my phone lit up at 2am with a Slack alert.
Six months into using LLMs in production, I had a classification pipeline that was wrong about 30% of the time.
Three months ago my team needed to automate a code review pipeline β pull a PR, analyze it across security, performance, and readability dimensions, then g
Three months ago, my team was building an internal tool that needed to summarize support tickets, suggest fixes from error logs, and draft reply templates