Building Production-Grade RAG Pipelines

· 6 min read
AI RAG engineering

Most RAG demos work. Most RAG systems in production don't — at least, not well enough. Here's what I've learned building them at scale.

The Gap Between Demo and Production

A prototype RAG pipeline is easy to build. You embed some documents, store them in a vector DB, retrieve the top-k chunks, and feed them into an LLM. Done in an afternoon.

The problems start when you hit real data:

  • Inconsistent chunk quality. Your documents aren't uniform. A PDF with tables, a webpage with navigation noise, a Word doc with tracked changes — they all need different parsing strategies.
  • Retrieval failures. Top-k cosine similarity often returns confident-looking results that are semantically wrong.
  • Context window mismanagement. Dumping 10 retrieved chunks into a prompt without ranking or filtering wastes tokens and confuses the model.

What Actually Works

1. Chunking Strategy Matters More Than You Think

Don't just split on token count. Use semantic chunking — break on logical boundaries (section headers, paragraph breaks, list items). For structured documents, preserve structure as metadata.

from langchain.text_splitter import RecursiveCharacterTextSplitter

splitter = RecursiveCharacterTextSplitter(
    chunk_size=512,
    chunk_overlap=64,
    separators=["\n\n", "\n", ". ", " "]
)

2. Hybrid Search Beats Pure Vector Search

Combine dense retrieval (embeddings) with sparse retrieval (BM25 keyword search). This handles both semantic similarity and exact term matching — critical when users search for specific names, codes, or technical terms.

3. Reranking Is Non-Negotiable

After retrieval, run a cross-encoder reranker over your top-20 results and keep only the top-5. Models like bge-reranker-large dramatically improve precision.

4. Evaluate Relentlessly

Build an evaluation set from day one. Measure: - Answer faithfulness — does the answer stay grounded in the retrieved context? - Answer relevance — does it actually answer the question? - Retrieval precision — did you retrieve the right chunks?

Use tools like RAGAS to automate this.

The Architecture I Use

Query → Query Expansion → Hybrid Retrieval → Reranking → Context Assembly → LLM → Answer

Each stage is observable and independently improvable. No black boxes.

Closing Thought

RAG is an engineering problem, not a prompt engineering problem. The quality of your retrieval pipeline determines 80% of your output quality. Invest there first.

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