AI Apps / RAG deployment guide: zero-ops edge publishing with Cloudflare Pages
Git-driven AI Apps / RAG deployment in practice: commit to build, atomic publish to every edge node.
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Quick answer
How do I fix hallucinations in RAG output?
Each chunk must carry a source URL. A topK that is too large dilutes context, too small misses key info — tune per use case.
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In-depth analysis
RAG (Retrieval-Augmented Generation) couples private-knowledge retrieval with LLM generation, the dominant pattern for shipping AI applications. Combined with a vector store and structured chunking, answers become traceable and verifiable. At deploy time hand the built artifact to the edge platform and lean on static fallback plus security headers to hold the line on stability.
// Minimal RAG loop: retrieve -> build context -> generate
const ctx = await vectorSearch(query, { topK: 4 }); // pull relevant chunks from the vector store
const prompt = 'Answer based on the following material: ' + ctx + ' Question: ' + query;
const answer = await llm.generate({ messages: [{ role: 'user', content: prompt }] });
// Each chunk must carry a source URL so a human can audit and trace the answerAI Apps / RAG Deployment Guide · Key takeaways
- astro.config has a single source-of-truth site value
- public/_headers declares nosniff and HSTS
- 404 returns a real 404
- Preview passes all 8 edge drills
FAQ & pitfalls
What is the cache strategy for RAG answers?
Vector search results cache on demand, prioritizing hit rate. Static knowledge pages go through edge long cache; dynamic retrieval does not enter the CDN.
Explore the hub
See all 5 guides in AI & RAGSame category as this article, sorted across the codebase.
AI Apps / RAG architecture: layered patterns for high-concurrency edge apps
The edge-delivery view of AI Apps / RAG's layering and data flow.
AI Apps / RAG best practices: engineering conventions for production
Field-tested AI Apps / RAG practices from production, avoiding the common anti-patterns.
AI Apps / RAG performance tuning: push first paint and edge response to the limit
Around AI Apps / RAG's real bottlenecks, an actionable LCP / INP / CLS checklist.
AI apps in practice: traceable answers with RAG
Combine vector retrieval with LLM generation using RAG (Retrieval-Augmented Generation) to ship traceable, verifiable enterprise Q&A.
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What readers are asking
Questions that keep coming back in reader feedback, already answered.
- 01
Why is my Astro component's interactivity not working?
Components ship as zero-JS HTML by default, so an interactive one needs a client:* directive (client:load, client:visible, …). Without it the script is never bundled into the page.
See the full guide → - 02
Why is my custom Tailwind v4 brand colour not applied?
Colours must be declared in @theme (for example --color-brand). A missing @tailwindcss/vite plugin, or a leftover v3 config, makes bg-brand silently render nothing.
See the full guide → - 03
Why are _headers / _redirects not taking effect?
Both files must sit at the root of the output directory (dist/); anything under src/ is never uploaded. A purely static project should not add _worker.js either — that gives up the zero-cold-start advantage.
See the full guide →