RAG content - Tech@Hoomanely

Software

Retrieval Cost Engineering for OpenSearch RAG

A production RAG system feels “fast” when it’s consistent: answers arrive quickly, follow-ups don’t suddenly slow down, and costs don’t jump with traffic. The biggest lever to achieve that isn’t usually the model—it’s retrieval. When retrieval is engineered like a disciplined subsystem (with budgets,

High-Frequency RAG: Designing Retrieval Pipelines That Survive User-Interactive Latency Budgets

Interactive AI experiences are different from traditional RAG pipelines. They exist inside tight user-facing latency boundaries where even a 300–500 ms delay can change how users feel about the system. A chat interface that responds instantly feels intelligent; one that hesitates feels broken. When retrieval sits on the hot

Knowledge Base Release Trains: Shipping RAG Content with the Same Discipline as Code

Retrieval-Augmented Generation (RAG) systems rarely fail loudly. They fail in subtler, stranger ways: a support answer sounds different today than yesterday; a troubleshooting step the assistant always got right suddenly drifts; or the bot becomes strangely confident about something no one remembers teaching it. When you dig deeper, the underlying