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 — 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 — 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 — 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. It’s 30.
Three weeks ago I got annoyed.
Last November, my phone lit up at 2am with a Slack alert.
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
Three months ago my team needed to automate a code review pipeline — pull a PR, analyze it across security, performance, and readability dimensions, then g
Six months into using LLMs in production, I had a classification pipeline that was wrong about 30% of the time.
The promise was always there: AI inference on your own hardware, your own terms, no API bills.
You’ve shipped a proof-of-concept with GPT-4, the demo went well, and now engineering leadership wants it in production by next quarter.
Three tools are fighting for the center of your development workflow. One costs $20/month and works inside VS Code.
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