Building Production-Ready RAG Systems

Retrieval-Augmented Generation (RAG) has become the standard pattern for building LLM applications that need access to private or up-to-date information. But moving from a demo to production requires careful attention to retrieval quality, latency, and reliability.

RAG Architecture Overview

Chunking Strategies

How you chunk your documents dramatically impacts retrieval quality. We recommend semantic chunking that respects document structure, with chunk sizes of 256-512 tokens and 50-token overlaps.

from oneml.rag import SemanticChunker

chunker = SemanticChunker(
    chunk_size=512,
    chunk_overlap=50,
    respect_sentence_boundaries=True,
    min_chunk_size=100
)

chunks = chunker.chunk(document)

Hybrid Search

Combining dense (embedding-based) and sparse (keyword-based) retrieval consistently outperforms either alone. We recommend a 70/30 weighting toward dense retrieval, adjustable based on your domain.

Evaluation and Monitoring

Track key metrics including retrieval precision/recall, answer relevance, faithfulness (is the answer grounded in retrieved context?), and latency percentiles. Set up automated alerts for quality degradation.

Common Pitfalls

• Chunks too large lose semantic precision; too small lose context
• Not handling document updates (stale embeddings)
• Ignoring retrieval latency in p99 calculations
• Missing guardrails for hallucination detection
• No fallback when retrieval returns low-confidence results