Building Production-Ready RAG Systems with LangChain

Introduction

Retrieval-Augmented Generation (RAG) has emerged as one of the most practical applications of large language models in enterprise settings. Unlike fine-tuning, RAG allows organizations to leverage their proprietary data without the computational overhead of model training.

The Architecture

A production RAG system consists of several key components:

1. Document Processing Pipeline

Your documents need to be chunked intelligently. We recommend:

Semantic chunking: Split on natural boundaries like paragraphs and sections

Overlap: Use 10-20% overlap between chunks to maintain context

Metadata preservation: Keep source information attached to each chunk

from langchain.text_splitter import RecursiveCharacterTextSplitter
splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200,
separators=["\n\n", "\n", ". ", " "]
)

2. Vector Database Selection

Choose based on your scale:

3. Retrieval Strategy

Simple similarity search often falls short. Consider:

Hybrid search: Combine semantic and keyword search

Re-ranking: Use a cross-encoder to refine top results

Query expansion: Generate multiple query variations

Evaluation Metrics

Track these metrics in production:

Retrieval precision: Are the right documents being found?

Answer faithfulness: Does the response align with retrieved context?

Latency: End-to-end response time under load

Conclusion

Building production RAG systems requires careful attention to each component. Start simple, measure everything, and iterate based on real user feedback.