Azure AI Search Advanced RAG with Terraform: Hybrid Search, Semantic Ranking, and Agentic Retrieval 🧠

Vector search alone leaves relevance on the table. Hybrid search with semantic ranking, chunking strategies, metadata filtering, strictness tuning, and the new agentic retrieval pipeline turn Azure AI Search into a production RAG system. All wired through Terraform. In RAG Post 1 , we deployed Azure AI Search with a basic index and connected it to Azure OpenAI. It works, but retrieval quality is mediocre. Your users ask nuanced questions and get incomplete or irrelevant answers. The fix isn't a better generation model. It's better retrieval. Azure AI Search has the most sophisticated built-in retrieval pipeline of the three major clouds: hybrid search combining BM25 keyword matching with vector similarity via Reciprocal Rank Fusion (RRF), a transformer-based semantic ranker for deep re-scoring, metadata filtering, strictness controls, and a new agentic retrieval mode that decomposes complex queries automatically. This post covers the production patterns. 🎯 Important note: Azure OpenAI