Apr 2, 2025 · 7 min read
Building Agentic RAG Systems That Actually Work
Six months of production RAG taught me that retrieval quality dominates, not the LLM. Here is how we improved accuracy by 35%.
RAGLangGraph
Agentic RAG becomes useful when the system can inspect its own uncertainty and choose the next retrieval step deliberately.
The largest quality gains came from better chunking, corpus-specific embeddings, and benchmark sets that mirrored real user questions. The agent layer only helped after the retrieval layer was trustworthy.
LangGraph made the orchestration explicit enough to test each decision point, which turned vague prompt tuning into a measurable engineering loop.