What are the patterns for integrating LLMs into existing SaaS products?

LLM integration into SaaS products follows established patterns: copilot (AI assistant embedded in the product UI that helps users accomplish tasks), smart defaults (AI suggests values for form fields, configurations, or settings), content generation (AI creates drafts of emails, reports, descriptions), intelligent search (natural language query over product data using RAG), and automation (AI triggers actions based on pattern recognition). Implementation architecture: create an AI service layer that abstracts LLM provider specifics, implement prompt templates per feature with version control, add a caching layer for repeated queries, and build a feedback mechanism for continuous improvement. UI/UX patterns: inline suggestions (ghost text that users can accept or dismiss), command palette (slash commands or natural language input), side panel copilot (persistent AI assistant), and background automation (AI acts silently with user notification). Technical considerations: handle streaming responses for real-time UX, implement token limits per user tier (freemium vs premium), queue requests during high load with graceful degradation, and A/B test AI features to measure business impact. Monetization: AI features as premium tier differentiator, usage-based pricing for high-consumption features, and AI credits system for metered access. Privacy: clearly communicate what data is sent to LLM providers, offer opt-out for sensitive workspaces, and consider on-premise LLM deployment for enterprise customers.