Welcome back to TechTalks with Manoj — the show where we unpack the tech that’s actually shaping modern systems — not just trending on social feeds.
In today’s episode, we’re diving deep into a service that doesn’t get nearly enough credit: Azure AI Search.
You’ve probably heard of vector search. Maybe semantic search. But Azure AI Search? It’s doing all of that — and then some — powering everything from hybrid retrieval to LLM grounding, and transforming how enterprises mine value from unstructured data.
This isn’t “just” search. It’s an intelligent retrieval engine — stacked with full-text, vector, semantic, and hybrid capabilities — plus a built-in AI enrichment pipeline that turns PDFs, blobs, and images into knowledge-ready chunks.
Here’s what we’re covering:
The full retrieval stack — from Lucene to vectors to semantic reranking
How hybrid search + semantic captions give LLMs real-world grounding
When to use built-in vs custom enrichment, and how to host your own skills
Why Reciprocal Rank Fusion (RRF) changes the game for RAG precision
Practical tips for scaling, caching, and index tuning in production
Security, compliance, and when not to use Customer Managed Keys
And how to build an enterprise-grade, RAG-ready architecture using Azure AI Search, OpenAI, and your own data lake
If you're building copilots, internal bots, or search experiences that actually work — Azure AI Search is your silent MVP.
Let’s get into it.
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