8 min read
DiskANN in Azure SQL Is GA: Do You Still Need a Separate Vector Store?
DiskANN vector indexes are now GA in Azure SQL and Fabric SQL. When embeddings belong next to your rows, and when Azure AI Search still wins.
Azure AI Search
34 articles
9 min read
RAG Citations People Can Check: Treat Them as Part of the Answer
Good retrieval is wasted if users cannot verify the answer, so design RAG citations to be checkable, validated in code and measured like relevance.
8 min read
Hybrid Search for RAG: Test What the BM25 Leg Rescues
Hybrid search is default RAG advice, but the BM25 leg has real costs; a three-way test on Azure AI Search shows which questions it rescues.
8 min read
Treat Retrieval Changes Like Schema Changes: A RAG Regression Gate
Make retrieval-first a team rule: an owned question set, a side-by-side index comparison on Azure AI Search, and a gate that fails on lost answers.
8 min read
Is Your Reranker Earning Its Place? Test It Before You Pay for It
The semantic ranker only reorders the top 50 results. A with-and-without test on a labelled set shows which RAG queries it rescues and which it just slows.
7 min read
Wrong RAG Answer? Rule Out Retrieval Before You Edit the Prompt
Most wrong RAG answers start with the passages, not the prompt. A labelled question set and a retrieval-only triage on Azure AI Search show which one to fix.
8 min read
Agentic Retrieval or a Hand-Built RAG Pipeline? How I Decide
Azure AI Search knowledge bases now plan queries for you, in preview. When to hand over the RAG pipeline, when to keep building it, and how to test both.
9 min read
Six Vector Stores for RAG and Where Each One Hurts
Azure AI Search, DocumentDB, Cosmos DB for NoSQL, pgvector, Pinecone and Redis each fail differently in RAG. Where each one hurts in January 2026.
9 min read
Seven RAG Failure Modes and the Pattern That Fixes Each
Diagnose why a RAG system fails before adding patterns: hybrid search, semantic reranking, grounding gates and citations on Azure AI Search, mapped to failures.
1 min read
Vector Database Selection: Comparing Azure AI Search, Cosmos DB, and PostgreSQL
Azure AI Search, Cosmos DB with vector indexing, and Azure Database for PostgreSQL with pgvector each provide vector search capabilities with different…
1 min read
Azure AI Search: Implementing Hybrid Search for Better RAG Results
Pure vector search excels at semantic similarity but can miss exact keyword matches. Pure keyword search finds exact terms but misses conceptually similar…
1 min read
Azure AI Search: Implementing Hybrid Search with Vectors and Keywords
Hybrid search significantly improves retrieval quality for RAG applications by leveraging both semantic understanding and exact keyword matching.
1 min read
Vector Database Selection: Choosing the Right Solution for RAG
The best vector database depends on your specific requirements. Azure AI Search excels for hybrid search with integrated semantic ranking. Pinecone offers…
1 min read
Hybrid Search: Combining Vector and Keyword Search in Azure AI Search
Vector search excels at semantic similarity but can miss exact matches. Keyword search finds precise terms but misses synonyms and context. Together, they…
1 min read
Vector Database Selection: Comparing Azure AI Search, Pinecone, and Qdrant
Best for organizations already invested in Azure with hybrid search requirements. Pros: Hybrid search, semantic ranking, enterprise security, managed…
1 min read
Implementing Hybrid Search in Azure AI Search
Keyword search excels at exact matches and rare terms. Vector search captures semantic similarity. Combining them leverages both strengths while mitigating…
1 min read
Building RAG Applications with Azure AI Search and GPT-4o
RAG works by first retrieving relevant documents from a search index, then passing those documents as context to an LLM for generation. This approach…
1 min read
Building AI-Powered Search with Azure AI Search and Semantic Ranker
Monitor reranker scores and caption extraction quality. A/B test semantic ranking against pure vector or keyword search to quantify improvements for your…
1 min read
Multimodal RAG: Combining Text, Images, and Tables for Enhanced Search
Multimodal RAG ensures users find relevant information regardless of how it's represented in the source documents.
1 min read
Azure AI Search Vector Optimization: Reducing Costs While Improving Recall
Vector search costs can spiral quickly at scale. After optimizing Azure AI Search deployments processing 50 million vectors, I've identified key patterns…
7 min read
Multimodal RAG on Azure: Caption PDF Figures Before You Embed Them
Make diagrams and charts in PDFs retrievable with Document Intelligence 4.0 figure extraction, GPT-4o descriptions and Azure AI Search hybrid queries.
8 min read
Where Should Your Vectors Live on Azure? Search Index vs Database
Azure AI Search, Cosmos DB for NoSQL and PostgreSQL with pgvector can all hold embeddings now. Here is how I decide between a search index and your database.
8 min read
RAG Beyond Top-K: Retrieval Upgrades Worth Making in January 2025
Which RAG retrieval upgrades Azure AI Search gives you as GA or preview in January 2025, which you build yourself, and the order I'd add them in.
7 min read
Azure AI Search Index Projections: Keys, Deletes and Chunk Order
The edge cases of index projections in Azure AI Search: what the chunk index schema needs, how keys change on re-index, deletes, and keeping chunk order.
8 min read
Azure AI Search Index Operations: Schema Changes, Rebuilds and Aliases
Which Azure AI Search index changes work in place, which force a rebuild, and how to swap versioned indexes, load documents and check health safely.
7 min read
Tuning Hybrid Search in Azure AI Search: What RRF Lets You Change
Which hybrid search knobs Azure AI Search exposes on the 2023-11-01 API, why I avoid client-side score weighting, and how to measure changes before shipping.
7 min read
Keeping Azure AI Search Vector Indexes Small Without Compression
Azure AI Search has no built-in vector compression yet. How to estimate vector index size, read the quota, and cut it with chunking and dimension choices.
7 min read
Azure AI Search Semantic Ranker: Configuration, Thresholds and Cost
How to design semantic configurations, use reranker scores as a RAG cut-off, and decide when Azure AI Search's semantic ranker is worth paying for.
7 min read
Integrated Vectorization in Azure AI Search: What It Does and Doesn't Do
What integrated vectorization in Azure AI Search actually removes from a RAG pipeline, what it leaves behind, and when I'd still embed in my own code.
7 min read
Azure AI Search After Ignite 2023: What's GA and What's Still Preview
A January 2024 stock-take of Azure AI Search for RAG: which features are GA, which are preview, and which SDK and REST versions to build on.
6 min read
Self-Correcting RAG: Grade Retrieval Before You Answer
How to add retrieval grading, query rewriting, and groundedness checks to RAG on Azure OpenAI and Azure AI Search without tripling latency or cost.
6 min read
Structure-Aware Chunking for RAG: Headings, Tables and Breadcrumbs
Chunk documents along their headings, keep tables whole and prefix every chunk with its section path, using Document Intelligence markdown output and Python.
7 min read
A RAG Maturity Model: Diagnosing Where Your System Falls Short
A five-level RAG maturity model for Azure AI Search and Azure OpenAI, covering what each level fixes, what it costs, and when to stop climbing.
7 min read
Enterprise AI in 2024: Five Bets Against What Is GA on Azure
Five enterprise AI bets for 2024, each checked against what is generally available or still in preview on Azure as of 1 January 2024.