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Generative AI · October 7, 2025

RAG Architecture Explained: Build a Production-Grade Retrieval System

A reference architecture for retrieval-augmented generation, with the seven failure modes that kill RAG in production and how to design around each one.

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October 7, 2025

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  1. 01What is RAG, really?
  2. 02The five-stage reference pipeline
  3. 03Pick your vector database with intent
  4. 04The seven failure modes that kill RAG in production
  5. 05A minimal retrieval call you can ship today
  6. 06How to evaluate a RAG system
  7. 07Cost reality check

01What is RAG, really?

Retrieval-Augmented Generation is a pattern where the model retrieves relevant documents from a vector store before generating an answer. It trades a portion of the model's training knowledge for fresh, citable, domain-specific context.

02The five-stage reference pipeline

Every production RAG system has the same shape. The art is in the parameters at each stage.

  1. 1

    Ingestion

    Pull source documents on a schedule. Track versions. Hash before re-indexing so unchanged docs don't regenerate embeddings.

  2. 2

    Chunking

    Split documents into semantic units. Target 200–500 tokens with 10–15% overlap. Honor headings.

  3. 3

    Embedding

    Encode chunks with a domain-appropriate model. Re-evaluate the embedding model annually; the quality gap moves fast.

  4. 4

    Retrieval

    Hybrid search (dense + BM25 lexical) beats pure vector for most enterprise corpora. Re-rank top 40 to top 6.

  5. 5

    Generation

    Inject retrieved chunks into the prompt with provenance. Require the model to cite the chunk id.

03Pick your vector database with intent

There is no universally best vector database. There is a best one for your scale, latency budget, and ops culture.

Managed, fast, expensive at scale. Pick when you do not want to operate infrastructure and your corpus is under 100M vectors.

04The seven failure modes that kill RAG in production

These are in order of frequency, from the cases we see most often.

  1. Chunks too large — context dilutes, model picks the wrong sentence.
  2. No re-ranker — vector similarity returns plausible but wrong neighbors.
  3. Single-vector search — you miss synonyms; lexical retrieval would have caught them.
  4. No provenance — you cannot tell which chunk produced the answer, so you cannot debug.
  5. Stale index — docs updated, embeddings did not regenerate.
  6. Bad embeddings for your domain — generic English embedder fails on code, contracts, or biomedical text.
  7. Hallucinated citations — model invents source ids. Validate every citation against the retrieved set before showing the user.

05A minimal retrieval call you can ship today

hybrid retrieval + rerank
def retrieve(query, k=6):
    # Dense (semantic) + sparse (lexical) candidates
    dense_hits = vector_db.search(embed(query), top_k=20)
    sparse_hits = bm25_index.search(query, top_k=20)
    candidates = dedupe(dense_hits + sparse_hits)

    # Cross-encoder rerank for final precision
    scored = reranker.score([(query, c.text) for c in candidates])
    ranked = sorted(zip(candidates, scored), key=lambda x: -x[1])
    return [c for c, _ in ranked[:k]]

06How to evaluate a RAG system

You need three eval suites: a retrieval set (does it pull the right chunks?), an answer set (is the response correct given the chunks?), and a citation set (does it point at the source it actually used?).

Quick Quiz

Your RAG system retrieves the correct chunks 90% of the time, but answer accuracy is only 65%. What is the likeliest culprit?

07Cost reality check

$0.002

Avg cost per query (V2)

450ms

P95 retrieval latency

93%

Citation-grounded answers

Need a RAG system that actually works in production?

We design and ship hybrid retrieval pipelines in weeks, not quarters.

Get a RAG audit

The takeaway

RAG is hybrid retrieval + reranking + grounded generation with provenance. Get those three right and 80% of the failure modes disappear.

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