Code, Cloud & Context

Introduction: Query understanding is the critical first step in building intelligent AI systems that respond appropriately to user requests. Before your system can retrieve relevant documents, call the right tools, or generate helpful responses, it needs to understand what the user actually wants. This involves intent classification (is this a question, command, or conversation?), entity […]

Introduction: Hybrid search combines the best of both worlds: the semantic understanding of vector search with the precision of keyword matching. Pure vector search excels at finding conceptually similar content but can miss exact matches; pure keyword search finds exact terms but misses semantic relationships. Hybrid search fuses these approaches, using vector similarity for semantic […]

Introduction: LLM APIs fail. Rate limits hit, services go down, models return errors, and responses sometimes don’t meet quality thresholds. Building reliable AI applications requires robust fallback strategies that gracefully handle these failures without degrading user experience. A well-designed fallback system tries alternative models, implements retry logic with exponential backoff, caches successful responses, and provides […]