Retrieval-Augmented Generation promises accurate, up-to-date LLM responses. Building RAG for production requires solving hard problems most tutorials skip entirely.
RAG (Retrieval-Augmented Generation) has become the dominant pattern for building LLM-powered applications over private or specialised knowledge. But most tutorials show toy examples that break immediately in production.
Here are the real challenges we solve when building production RAG systems.
Chunk size matters more than you think. Too small and you lose context; too large and you reduce retrieval precision. The optimal chunk size depends on your content type — technical documentation needs different chunking than conversational FAQ content.
Embedding model selection is critical. OpenAI text-embedding-3-large outperforms most alternatives, but for cost-sensitive applications or domain-specific content, fine-tuned models often perform better.
Reranking dramatically improves precision. A two-stage retrieval approach — BM25 or vector search for recall, cross-encoder reranking for precision — consistently outperforms single-stage retrieval.
Evaluation is non-negotiable. Build a golden dataset of question-answer pairs and evaluate retrieval quality, generation quality, and end-to-end task completion before deploying.