AI Apps / RAG hands-on tutorial: from zero to your first runnable project
Step-by-step walkthrough of AI Apps / RAG with copy-paste commands, directory layout, and a first runnable artifact.
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Quick answer
How do I lock RAG vector store versions?
Embedding model and vector store versions are tightly coupled. Bump the embedding model together with a full index rebuild, or recall drifts.
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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. Going from zero to a first runnable project is about locking in the smallest end-to-end loop before you scale.
// 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 Hands-on Tutorial · Key takeaways
- Project initialized with the official scaffold
- Dependencies pinned to a single pnpm lockfile
- Local dev server starts and first paint is reachable
- First runnable commit pushed
FAQ & pitfalls
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.
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 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.
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.
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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 →