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Building Production RAG Systems: Best Practices for 2026
Production RAG is iterative. Start simple, measure everything, and improve based on real user feedback.
Vector Search
37 articles
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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…
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Implementing RAG with LangChain and Azure OpenAI
Experiment with chunk sizes, overlap, and retrieval strategies. Hybrid search combining semantic and keyword matching often provides the best results for…
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Azure Cosmos DB for AI Applications: Vector Search and Change Feed Patterns
Use Change Feed to trigger embedding generation and synchronization with other AI services automatically when documents are created or updated.
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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.
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Semantic Caching for LLM Applications: Reducing Costs and Latency
Unlike exact-match caching, semantic caching uses embeddings to find similar queries even when worded differently.
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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
Embedding Models: Choosing Between OpenAI, Azure, and Open Source
The text-embedding-ada-002 and newer text-embedding-3 models provide high-quality embeddings with minimal setup.
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…
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RAG Chunking Strategies: Finding the Optimal Approach
The simplest approach splits text at regular intervals with optional overlap. Semantic chunking respects content boundaries like paragraphs and sections.
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Cosmos DB Vector Search: Implementing Semantic Search at Global Scale
Configure your Cosmos account with write regions near your users and read replicas globally. Vector searches automatically route to the nearest replica…
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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…
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Vector Search Optimization: Scaling to Billions of Vectors
Quantization and HNSW tuning enable vector search at billion-scale with reasonable latency.
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Hybrid Search Implementation: Combining Vector and Keyword Search
Hybrid search delivers better results than either approach alone. Implement it early in your RAG pipeline and tune the weights based on your specific use case.
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RAG 2.0: Advanced Retrieval Patterns for Production AI
RAG 2.0 is about precision and reliability. Invest in retrieval quality, and your generation quality will follow.
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OneLake AI Workloads: Running AI Directly on Your Data Lake
OneLake AI Workloads bring AI capabilities directly to your data, eliminating data movement and enabling efficient large-scale AI processing.
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Filtered Vector Search in Azure AI Search: Best Practices
Pre-filtering: Filter before vector search Post-filtering: Filter after vector search Azure AI Search uses pre-filtering with automatic optimization.
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HNSW Tuning for Azure AI Search: A Practical Guide
HNSW builds a multi layer graph where: Higher layers : Sparse, for fast long range navigation Lower layers : Dense, for precise local search Layer 0 :…
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Product Quantization for Extreme-Scale Vector Search
PQ divides each vector into subvectors and quantizes each independently: For even faster search, combine with inverted file index: Dataset Size n subvectors…
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Scalar Quantization for Vector Search: A Practical Guide
Scalar quantization maps floating point values to integers by dividing the value range into buckets: Understanding quantization error helps set…
2 min read
Binary Vectors and Quantization in Azure AI Search
With quantization, this can be reduced to 1-2 GB.
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Improved Vector Search in Azure AI Search: Deep Dive
Vector search finds similar items by comparing their mathematical representations (embeddings). The key components: Embeddings : Dense vectors representing…
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Azure AI Search Updates: What's New in Summer 2024
Expanded Vector Dimensions Semantic Ranking Improvements Customer-Managed Keys for Vectors
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Databricks Vector Search Deep Dive: Production Patterns
Taking Vector Search to production requires careful consideration of performance, reliability, and maintenance. This guide covers production-ready patterns.
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Vector Search in Databricks: Semantic Search at Scale
Databricks Vector Search enables semantic similarity search over your lakehouse data. Build RAG applications, recommendation systems, and intelligent search…
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Semantic Caching: Intelligent Response Reuse for LLMs
1. Start conservative Higher threshold (0.95+) for critical applications 2. Tune with data Evaluate on your actual query patterns 3. Monitor quality Track…
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Hybrid Search Optimization: Combining Vector, Keyword, and Semantic Search
Combining vector, keyword, and semantic search solved many relevance problems for us. This post distils the hybrid strategies I used to get the best of each…
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Vector Compression in Azure AI Search: Reducing Costs Without Sacrificing Quality
Vector compression reduced storage costs dramatically in a production index I worked on. Here are the practical trade-offs and configuration tips I used to…
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Integrated Vectorization in Azure AI Search: Simplifying RAG Pipelines
Integrated vectorization removed an entire pipeline step in a recent project. I'll show how it simplifies RAG pipelines and where to be cautious when…
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Azure AI Search Updates: January 2024 Features and Capabilities
I've been integrating Azure AI Search into RAG systems; the January 2024 updates simplify common workflows. Below are the changes I judged most impactful…
2 min read
RAG Architecture Maturity Model: From Basic to Production-Grade
I've seen hundreds of RAG prototypes. The gap between a demo and a production-grade system usually comes down to retrieval quality, freshness, and…
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RAG Improvements: Advanced Retrieval Techniques
Advanced techniques for improving Retrieval-Augmented Generation systems for better accuracy and relevance.
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Hybrid Retrieval Patterns for RAG Applications
Hybrid retrieval improves RAG quality by combining the strengths of different search approaches. Tomorrow, I will cover Azure Dev Box and development…
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Azure Cognitive Search Vector Search: Building Semantic Search Applications
Vector search enables powerful semantic search capabilities. Tomorrow, I will cover hybrid retrieval patterns in more detail.
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Azure Cognitive Search Vector Search Preview: Native Azure Vector DB
Native Azure Integration : Works seamlessly with Azure services Hybrid Search : Combine vectors with full text search Enterprise Ready : Security,…
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Hybrid Search Patterns in Azure Cognitive Search
Hybrid search patterns enable building search systems that handle diverse query types effectively.
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Vector Search Preview in Azure Cognitive Search
Vector search enables powerful similarity-based retrieval that complements traditional keyword search.