← Back to Blog

RAG at Runtime: Retrieval & Generation Without the Buzzword Soup

•10 min read•AI & Machine Learning

Learn the runtime half of RAG - how to retrieve relevant chunks and generate grounded answers. Practical guide to building production-ready retrieval and generation systems.

RAG at Runtime: Retrieval & Generation Without the Buzzword Soup

•10 min read•Technical Guide

This is the follow-up to our indexing post. Once your data is loaded, split, embedded, and stored, the real show begins: retrieval and generation. This is the runtime half of RAG—the part users experience as "it just knows."

Related Reading: New to RAG indexing? Check out our comprehensive guide on

RAG Indexing, Demystified

first.

The Runtime Loop (At a Glance)

User Query
 ↓
Embed Query  →  Retrieve Top-K Chunks  →  (Optional) Re-rank  →  Build Prompt
                                                      ↓
                                            Generate Grounded Answer
  • Retrieval: Find the most relevant chunks from your indexed store.
  • Generation: Ask the LLM to answer using those chunks—grounded, concise, and cited.

Swap in your preferred libraries for embed, vector_store.search, and generate. The orchestration stays the same: take a user question, retrieve the right context, and generate a grounded answer with citations.

Minimal Pseudocode

async function answer(question) {
  const qvec = await embed(question)
  const chunks = await vector_store.search(qvec, { k: 8 })
  const reranked = await rerank(question, chunks)
  const prompt = buildPrompt(question, reranked)
  return await generate(prompt)
}

Quality Tips

  • Prefer semantic search over pure keyword search.
  • Re-rank results for better precision on long queries.
  • Keep prompts tight; include citations explicitly.
  • Cache embeddings and retrieval where possible.

Want help implementing this in production? Contact us — we build reliable, observable RAG systems tailored to your stack.