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
Artículos relacionados
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.
IA agéntica frente a automatización tradicional: por qué importa la distinción
Entender el espectro — de la automatización basada en reglas a los copilotos y a los agentes totalmente autónomos — y por qué las empresas necesitan IA que actúe en lugar de solo sugerir.
La revolución de la memoria: cómo los agentes con contexto transforman las operaciones
De los prompts sin estado a la memoria persistente: cómo los agentes con contexto de largo plazo entregan resultados de negocio que los sistemas LLM tradicionales no pueden alcanzar.
Comentarios
Aún no hay comentarios. ¡Sea el primero!