Building Production-Ready RAG Applications

Retrieval-Augmented Generation (RAG) has become the go-to pattern for building AI applications that need accurate, up-to-date information. But moving from prototype to production requires careful attention to several critical areas.

What is RAG? RAG combines the power of large language models with your organization's proprietary data, enabling AI to provide accurate, contextual answers grounded in your documents.

The Foundation: Document Processing Pipeline

Your RAG system is only as good as your document processing pipeline. Here are the critical considerations:

Chunking Strategy

The way you split documents dramatically affects retrieval quality:

Semantic chunking — Split based on meaning, not arbitrary character counts
Hierarchical chunking — Maintain parent-child relationships for context
Overlap strategy — 10-20% overlap prevents context loss at boundaries

# Example: Semantic chunking with LangChain
from langchain.text_splitter import RecursiveCharacterTextSplitter

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

Metadata Extraction

Enrich every chunk with:

Source document and section information
Creation and modification dates
Hierarchical context (chapter → section → subsection)
Entity tags for filtering

Vector Store Selection Guide

Choose based on your scale and requirements:

Solution	Best For	Considerations
Pinecone	Managed scaling, enterprise	Higher cost, excellent performance
Weaviate	Open source, hybrid search	Self-hosted option available
pgvector	Existing PostgreSQL shops	Simpler ops, good for <1M vectors
Qdrant	High performance filtering	Great for complex queries

Evaluation Framework

Production RAG systems need rigorous, automated evaluation:

Critical: Never deploy a RAG system without establishing baseline metrics. What gets measured gets improved.

Key Metrics to Track

Retrieval Accuracy — Are we finding the right documents?
Answer Faithfulness — Does the response accurately reflect the retrieved content?
Hallucination Rate — How often does the model make things up?
Latency (P50/P99) — Response time at various percentiles

Production Checklist

Before going live, ensure you have:

☐ Automated document ingestion pipeline
☐ Incremental update support (not full re-index)
☐ Monitoring and alerting on quality metrics
☐ Fallback handling when retrieval fails
☐ Rate limiting and cost controls
☐ User feedback collection mechanism

"The difference between a demo and production RAG is about 80% of the work. Don't underestimate the engineering required."

Next Steps

Ready to build production RAG? Talk to our AI engineering team about your specific requirements.

Building Production-Ready RAG Applications: A Complete Guide