63 articles tagged with LLM.
A practical embedding model upgrade guide for RAG systems, built from a real support-search migration that initially reduced answer quality instead of improving it.
A real-world guide to prompt versioning and regression testing for production AI features, focused on preventing the subtle changes that hurt quality long before anyone notices.
A search-friendly guide to RAG retrieval quality evaluation, based on the moment one production assistant started citing stale documents and the team had to prove what 'good retrieval' meant.
A practical production playbook for AI systems: evaluation gates, guardrails, observability, cost control, and reliable release management.
A practical field manual for engineering teams who want AI features that survive real users, incidents, and budgets — not just demo day.
Use prompts to get reliable, safe outputs from LLMs for runbooks, code, and ops tasks.
Build MLOps pipelines for training, evaluation, and deployment. Reproducibility and monitoring.
We started routing 90% of LLM traffic through a small internal gateway. The gateway wasn't planned — it emerged from solving the same problem in 5 places. Here's the shape it took.
Prompt injection, data leakage, jailbreaks, and the boring controls that actually keep production AI features safe. The threat model that matters once you ship.
We benchmarked six embedding models on the same retrieval task. The results that surprised us, and how we'd pick today.
We cut our monthly LLM bill from $11,200 to $2,300 with seven specific changes. The ones that worked, the ones that didn't, and what we'd do first.
Fine-tuning is rarely the right answer. We've fine-tuned three times in two years; few-shot or RAG was correct for everything else. The decision criteria.
