From RAG to Production: Lessons Learned at Scale
Chunking Strategy Matters More Than Model Choice
The single highest-leverage decision in a RAG pipeline is how you chunk your source documents. Overlapping semantic chunks with metadata preservation consistently outperform fixed-size token windows, especially on heterogeneous corpora.
Hybrid Retrieval Beats Pure Vector Search
Combining BM25 keyword search with dense vector retrieval and a cross-encoder reranker produces significantly better recall than any single retrieval method. We see ten to twenty percent improvements in answer accuracy with this hybrid approach across every deployment.
Monitoring Retrieval Quality
In production, retrieval quality drifts as source documents are updated. We run automated evaluation suites nightly that compare retrieval results against curated test sets and alert when recall drops below acceptable thresholds.
ActiveMotion Team
AI Research
The ActiveMotion engineering and research team
Gerelateerde artikelen
Building Reliable AI Agents for Enterprise Workflows
How to design autonomous agents that handle real-world complexity, recover from failures, and integrate with existing enterprise systems at scale.
Agentische AI vs. klassieke automatisering: waarom het onderscheid telt
Het spectrum begrijpen — van regelgebaseerde automatisering over copiloten tot volledig autonome agents — en waarom bedrijven AI nodig hebben die handelt in plaats van alleen voorstelt.
De geheugenrevolutie: hoe context-bewuste agents operaties transformeren
Van stateless prompts naar persistent geheugen — hoe agents met langetermijncontext bedrijfsuitkomsten leveren die klassieke LLM-systemen niet halen.
Reacties
Nog geen reacties. Wees de eerste!