You know that moment when a slick proof-of-concept runs beautifully on your laptop, then utterly falls apart in production? That was us, thinking we were clever by wiring a large language model (LLM) into our R analytics pipeline—until the day our jobs started timing out, memory usage spiked, and our dashboards choked on error logs. If you’re eyeing LLMs to supercharge your R-based data workflows, our story might spare you from a few all-nighters. Why Even Bolt an LLM to R Analytics? We had a classic R pipeline: ingest, clean, analyze, visualize. But some of our data sources were unstructured—think user feedback, messy survey responses, or emails. Summarizing them manually was a slog. So, we thought: why not use an LLM (like OpenAI’s GPT or an open-source model) to summarize and categorize this text data on the fly, right inside our R scripts? For small test files, it was magic. But when we turned it loose on a few million records, everything broke.…