Building Production RAG Pipelines That Actually Work
A practical guide to RAG systems that hold up under real user load — chunking strategies, embedding choices, retrieval tuning, and hallucination guards.
Why Most RAG Tutorials Fail You
RAG demos look effortless. Embed some PDFs, stuff them in Pinecone, call GPT-4 — done. Then you hit production and everything falls apart: retrieved chunks are irrelevant, answers hallucinate facts that were right there in the document, and latency spikes kill the UX.
Here's what the tutorials leave out.
Chunking Is Your Foundation
The most overlooked variable in RAG quality is chunk strategy. Naive fixed-size chunking breaks semantic meaning at arbitrary byte boundaries.
# Bad: Fixed-size chunks break mid-sentence
text_splitter = CharacterTextSplitter(chunk_size=1000)
# Better: Semantic chunking respects structure
from langchain.text_splitter import RecursiveCharacterTextSplitter
splitter = RecursiveCharacterTextSplitter(
chunk_size=800,
chunk_overlap=120,
separators=["\n\n", "\n", ". ", " "],
)
Embedding Model Selection
OpenAI's text-embedding-3-small is not always the right choice. For domain-specific corpora (medical, legal, code), fine-tuned or domain-adapted models often outperform general embeddings by 15-30% on retrieval recall.
Retrieval: Hybrid Search Wins
Pure vector search misses exact keyword matches. Pure BM25 misses semantic variants. The solution: hybrid retrieval with RRF (Reciprocal Rank Fusion).
from langchain.retrievers import EnsembleRetriever
ensemble = EnsembleRetriever(
retrievers=[vector_retriever, bm25_retriever],
weights=[0.6, 0.4],
)
Hallucination Guards
Ground truth attribution is non-negotiable in production. Add a verification step that checks whether each claim in the answer is directly supported by the retrieved context.
Building reliable RAG is an engineering discipline, not a prompt engineering trick. Treat it like you'd treat any data pipeline: measure, iterate, instrument.