1 min read
RAG Systems That Hold Up: deciding where hybrid search is worth the complexity
I tightened system boundaries so quality checks trigger earlier, catching regressions before downstream systems consume bad data.
RAG
83 articles
1 min read
What Improved My RAG Pipeline: why citation UX matters as much as relevance
I worked on smoothing the handoff between data engineering and AI teams—standardizing feature contracts, embedding validation, and adding lightweight…
1 min read
What Improved My RAG Pipeline: deciding where hybrid search is worth the complexity
I worked on smoothing the handoff between data engineering and AI teams—standardizing feature contracts, embedding validation, and adding lightweight…
1 min read
RAG Engineering Log: fixing retrieval before touching prompts
I turned implicit processes into explicit operating rules—defining owners, acceptance tests, and lightweight runbooks so teams can move confidently and…
1 min read
RAG Systems That Hold Up: using reranking only where it changes outcomes
I spent the day reducing cognitive overhead for engineers and analysts—introducing clearer table contracts, simpler failure modes, and concise runbooks that…
1 min read
RAG Tradeoffs I Keep Seeing: fixing retrieval before touching prompts
I spent the day reducing cognitive overhead for engineers and analysts—introducing clearer table contracts, simpler failure modes, and concise runbooks that…
3 min read
Fine-Tuning vs RAG: The 2026 Decision Guide
In 2024, the community consensus was "RAG first, fine-tune never." In 2026, it's more nuanced.
1 min read
Building RAG Systems That Actually Work
This works for demos. It fails in production. Chunking matters more than you think. Random 500-token chunks lose context. A sentence about "the system"…
2 min read
RAG Patterns That Actually Work in Production
Vector search alone misses exact matches. Keyword search alone misses semantic similarity. Combine them.
1 min read
Building a Custom GPT for Your Documentation with Azure OpenAI
This pattern works for any documentation. The key is good chunking, quality embeddings, and a well-crafted system prompt that keeps the bot focused on your…
1 min read
Building Production RAG Systems: Best Practices for 2026
Production RAG is iterative. Start simple, measure everything, and improve based on real user feedback.
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
Prompt Engineering Patterns for Enterprise RAG Systems
Poorly designed prompts lead to hallucinations, ignored context, or responses that fail to meet business requirements. A systematic approach to prompt…
1 min read
GraphRAG: Combining Knowledge Graphs with Retrieval-Augmented Generation
GraphRAG excels when your questions involve multi-hop reasoning, relationship discovery, or when entity connections matter more than document similarity.
1 min read
Implementing Retrieval-Augmented Generation with Microsoft Fabric
Use Fabric notebooks to create interactive RAG applications that combine data exploration with AI-powered question answering over enterprise datasets.
1 min read
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…
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
Retrieval-Augmented Generation: Chunking Strategies for Better Results
The best chunking strategy depends on your document types. Technical documentation benefits from header-aware chunking. Conversational content works well…
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
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
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
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.
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
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
Evaluating RAG Systems: Metrics and Automated Testing with Azure AI
Fail builds when quality metrics drop below thresholds, catching regressions before they reach production.
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…
1 min read
Multimodal RAG: Retrieval Across Text, Images, and Documents
Multimodal RAG unlocks intelligence from documents containing text, tables, and images.
1 min read
Query Transformation Techniques: Improving RAG Recall
Query transformation is a high-leverage improvement for RAG systems.
1 min read
Reranking Strategies: Improving RAG Precision
Cross-encoder reranking typically improves RAG precision by 10-20%.
1 min read
Chunking Strategies for RAG: Finding the Right Granularity
Choose chunking strategy based on document structure and retrieval requirements.
1 min read
GraphRAG Implementation: Combining Graphs with Retrieval
GraphRAG excels at complex questions requiring synthesis across multiple documents.
1 min read
RAG Advanced Patterns: Beyond Basic Retrieval
Advanced RAG combines query expansion, hybrid retrieval, reranking, and context compression for better results.
1 min read
Multimodal RAG Advances: Images, Documents, and Beyond
Multimodal RAG unlocks knowledge trapped in visual formats. Start with document-heavy use cases where diagrams and charts carry critical information.
1 min read
Hybrid Search Improvements: Latest Techniques for Better Retrieval
Hybrid search continues to outperform pure vector or keyword approaches. Invest in tuning your fusion strategy for your specific domain.
1 min read
Multimodal RAG: Building Retrieval Systems for Images, Documents, and Text
Multimodal RAG opens up new possibilities for enterprise knowledge systems. Start with your most valuable visual content and expand from there.
1 min read
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.
1 min read
Vector Databases on Azure: Comparing Options for AI Applications
Best for: Enterprise search, RAG applications with complex filtering Best for: Global applications, multi-model data, transactional + vector workloads
1 min read
GraphRAG: Combining Knowledge Graphs with LLMs for Superior Retrieval
Invest in GraphRAG when your use case demands deeper understanding beyond surface-level text matching.
1 min read
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.
1 min read
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.
1 min read
Azure AI Search Updates: What's New in Summer 2024
Expanded Vector Dimensions Semantic Ranking Improvements Customer-Managed Keys for Vectors
1 min read
Memory and State Management in AI Agents
AI agents need memory to maintain context across interactions. Today I'm exploring how to implement effective memory systems.
1 min read
End-to-End RAG Evaluation: Complete System Assessment
Individual component metrics tell part of the story, but end-to-end evaluation measures how well your entire RAG pipeline performs as a system.
1 min read
Context Recall in RAG: Are You Finding All Relevant Documents?
While context precision measures noise in retrieved results, context recall measures completeness. Are you retrieving all the documents needed to fully…
1 min read
Context Precision in RAG: Evaluating Retrieval Quality
Context precision measures whether the retrieved documents are actually relevant to answering the question. High precision means less noise for the…
1 min read
Answer Relevancy in RAG: Does the Response Address the Question?
An answer can be factually correct and grounded in context but still fail to address what was actually asked. Answer relevancy measures how well the…
1 min read
Faithfulness Scoring in RAG: Detecting Hallucinations
Faithfulness is perhaps the most critical metric for RAG systems. An unfaithful answer that hallucinates information not in the source documents can be…
1 min read
Generation Metrics for RAG: Measuring Answer Quality
While retrieval metrics measure what documents are found, generation metrics evaluate the quality of the synthesized answer. This guide covers metrics…
1 min read
Retrieval Metrics for RAG: A Deep Dive
The retrieval component of RAG systems directly impacts generation quality. This guide provides a comprehensive overview of retrieval metrics and how to…
1 min read
RAGAS Framework: Automated RAG Evaluation
RAGAS (Retrieval Augmented Generation Assessment) is an open-source framework for evaluating RAG pipelines. This guide covers how to implement and use RAGAS…
1 min read
RAG Evaluation: Measuring Retrieval-Augmented Generation Quality
Retrieval-Augmented Generation (RAG) systems combine retrieval and generation components, each requiring specific evaluation strategies. This guide covers…
1 min read
Cohere on Azure: Enterprise Search and RAG Solutions
Cohere's models are now available on Azure AI, offering specialized capabilities for enterprise search and retrieval-augmented generation (RAG). This guide…
1 min read
Azure AI Search Index Projections: Chunking Documents at Index Time
Index projections let you chunk documents at index time — I used them to turn long manuals into searchable, LLM-friendly chunks. Here's a practical approach…
1 min read
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…
1 min read
Azure AI Search Semantic Ranker: Deep Dive and Optimization
Semantic ranking improved my search relevance considerably; the main cost was configuration complexity. This deep dive explains how I tuned the ranker for…
1 min read
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…
1 min read
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…
1 min read
Agentic RAG Patterns: Self-Correcting and Adaptive Retrieval
When RAG systems can reason about what to retrieve, they stop failing silently. My implementations of agentic RAG show how to add evaluation and iterative…
1 min read
Advanced Chunking Strategies for RAG: Beyond Fixed-Size Splits
In projects I've worked on, chunking decisions alone changed retrieval quality more than model choice ever did. This deep dive pulls together advanced…
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…
1 min read
Assistants Retrieval Tool: Building Knowledge Assistants
For many teams, the Assistants API's Retrieval tool removes the most tedious parts of building a knowledge assistant — you upload documents and the…
1 min read
Assistants API: File Handling and Knowledge Retrieval
I prefer the Assistants API's file-based retrieval when I need a fast, low-friction knowledge assistant — upload a set of documents and let the assistant…
2 min read
Building RAG Applications with Azure Cognitive Search and OpenAI
RAG with Azure Cognitive Search and Azure OpenAI is the production architecture I recommend most often for enterprise knowledge retrieval — not because it's…
1 min read
Multi-Index RAG: Querying Across Multiple Knowledge Sources
Implementing multi-index RAG systems to query and combine information from multiple knowledge sources.
1 min read
Recursive Retrieval for Complex RAG Queries
Implementing recursive retrieval strategies for handling complex, multi-step queries in RAG systems.
1 min read
Auto-Merge Retrieval for RAG Systems
Implementing auto-merge retrieval to automatically combine related chunks for comprehensive context.
1 min read
Sentence Window Retrieval for RAG Applications
Implementing sentence window retrieval to balance precision and context in RAG systems.
1 min read
Parent-Child Retrieval for RAG Systems
Implementing parent-child document retrieval to improve context and accuracy in RAG applications.
1 min read
Document Chunking Strategies for RAG Systems
Comprehensive guide to document chunking strategies for optimal retrieval in RAG applications.
1 min read
RAG Improvements: Advanced Retrieval Techniques
Advanced techniques for improving Retrieval-Augmented Generation systems for better accuracy and relevance.
1 min read
Vector Store Integrations: Choosing and Using Vector Databases
This concludes our August 2023 series on LLM optimization and vector stores.
1 min read
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…
2 min read
Copilot for Docs: Chat With Your Documentation
Copilot for Docs is one of the most concrete RAG applications Microsoft shipped publicly in this period—a conversational interface over documentation that…
1 min read
Implementing RAG with Azure Cognitive Search
RAG is the bridge between general-purpose LLMs and your specific enterprise data. Get it right, and you unlock tremendous value.
1 min read
Semantic Kernel Memory: Building AI with Long-Term Context
1. Organize into collections : Separate by topic/domain 2. Use meaningful IDs : Enable updates and deletions 3. Set appropriate relevance thresholds : Too…
1 min read
Re-ranking Search Results for Better RAG Performance
Cross encoders directly score query document pairs: Use an LLM to judge relevance: Using Cohere's specialized rerank API: 1. Retrieve more, re rank fewer :…
1 min read
Hybrid Search: Combining Vector and Keyword Search
A robust way to combine rankings: Adjust weights based on query characteristics: 1. Start balanced : 50/50 is often a good default 2. Tune on your data :…
1 min read
Document Chunking Strategies for RAG Systems
Simple but effective for uniform content: Respect sentence boundaries: Natural document structure: Use embeddings to find natural break points: Hierarchical…
1 min read
RAG Architecture Patterns: Building Production-Ready Systems
The basic pattern for straightforward use cases: Transform queries for better retrieval: Generate a hypothetical answer first, then use it for retrieval:…
1 min read
Introduction to Retrieval Augmented Generation (RAG)
RAG solves both by retrieving relevant context before generating responses.