AI & RAG
Here are 8 guides covering config examples, key takeaways, and troubleshooting.
All guides
AI apps in practice: turn private knowledge into traceable answers with RAG
AI apps RAG in practiceCombine vector retrieval with LLM generation using RAG (Retrieval-Augmented Generation) to ship traceable, verifiable enterprise Q&A.
AI Apps / RAG hands-on tutorial: from zero to your first runnable project
Ai App TutorialStep-by-step walkthrough of AI Apps / RAG with copy-paste commands, directory layout, and a first runnable artifact.
AI Apps / RAG quickstart: 5-minute local dev environment
Ai App QuickstartThe shortest path to getting AI Apps / RAG running — a minimal starting point for first-time users.
AI Apps / RAG deployment guide: zero-ops edge publishing with Cloudflare Pages
Ai App Deployment GuideGit-driven AI Apps / RAG deployment in practice: commit to build, atomic publish to every edge node.
AI Apps / RAG performance tuning: push first paint and edge response to the limit
Ai App Performance OptimizationAround AI Apps / RAG's real bottlenecks, an actionable LCP / INP / CLS checklist.
AI Apps / RAG best practices: engineering conventions for production
Ai App Best PracticesField-tested AI Apps / RAG practices from production, avoiding the common anti-patterns.
AI Apps / RAG architecture: layered patterns for high-concurrency edge apps
Ai App ArchitectureThe edge-delivery view of AI Apps / RAG's layering and data flow.
AI Apps / RAG troubleshooting: locating and fixing high-frequency failures
Ai App TroubleshootingThe most common production failures for AI Apps / RAG and their root-cause fixes.