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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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Thoughts on data engineering, cloud architecture, and technology. 2205 articles and counting.
Showing 24 of 2205 posts — page 14 of 92
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Building Production MLOps Pipelines with Azure Machine Learning
Automated pipelines ensure reproducibility, enable A/B testing, and provide audit trails for model governance.
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Cost Optimization Strategies for Azure OpenAI Workloads
Semantic caching avoids redundant API calls for similar queries. Reduce prompt length by removing unnecessary context. Use batch processing with the Batch…
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AI Observability with Azure Monitor and Application Insights
Monitor these essential AI metrics: latency (p50, p95, p99), token consumption per request, error rates by model and endpoint, cache hit rates for…
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Delta Lake Optimization: VACUUM, OPTIMIZE, and Z-ORDER Explained
VACUUM removes data files no longer referenced by the Delta log. Without regular vacuuming, your storage costs grow unbounded as old file versions accumulate.
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Implementing Semantic Kernel Plugins for Enterprise Applications
Native plugins expose C# methods to the kernel, allowing the LLM to invoke them when appropriate.
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Azure OpenAI o1 Models: Reasoning at Scale for Complex Problems
The o1 models are designed for tasks requiring deep reasoning: mathematical proofs, code debugging, scientific analysis, and strategic planning. They take…
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Microsoft Fabric Lakehouse: Unified Analytics for the Modern Data Stack
A Lakehouse stores data in open formats like Delta Lake while providing SQL query capabilities, ACID transactions, and schema enforcement. In Microsoft…
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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…
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Getting Started with Semantic Kernel 2.x: A Practical Introduction
Semantic Kernel is an SDK that integrates Large Language Models (LLMs) with conventional programming languages. Version 2.x introduces a more streamlined…
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End-to-End MLOps with Azure Machine Learning and GitHub Actions
Implement approval gates before production deployment. Track model lineage, performance metrics, and data dependencies for full auditability.
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Observability for LLM Applications: Tracing, Metrics, and Debugging
Log prompts and responses for failed interactions to a secure store for debugging. Implement sampling for successful calls to control storage costs while…
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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…
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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.
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Azure API Management for AI APIs: Rate Limiting and Cost Control
Configure Azure Monitor alerts when subscriptions approach quota limits. Provide self-service usage dashboards so customers can track their consumption and…
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Structured Output with JSON Mode: Reliable Data Extraction from LLMs
Structured outputs eliminate parsing errors. Add business validation for semantic correctness - the schema ensures format, you ensure meaning.
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Power BI Copilot: Natural Language Analytics for Business Users
Use descriptive table and column names that match business terminology. Add measure descriptions explaining what each calculation represents. Create…
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Implementing Semantic Caching for LLM Applications
Use Redis Vector Search for efficient similarity matching at scale. Tune the similarity threshold based on your use case - higher values ensure more precise…
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Building Knowledge Graphs with Azure OpenAI and Neo4j
Graph RAG significantly improves answers for questions involving relationships, hierarchies, and multi-hop reasoning.
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Azure Functions with AI: Serverless Inference Patterns
Use consumption-based billing to pay only for actual inference time. Implement caching for repeated queries and batch similar requests to amortize cold…
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Claude Extended Thinking: Solving Complex Reasoning Problems
Enable extended thinking for mathematical proofs, code analysis, strategic planning, and multi-constraint optimization problems. For simple queries…
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Data Lakehouse Architecture with Microsoft Fabric: Medallion Pattern Implementation
Use Delta Lake constraints and expectations to ensure data quality at each layer. Fabric's Data Quality monitoring provides visibility into quality metrics…
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Streaming LLM Responses: Patterns for Real-Time User Experience
Add a blinking cursor effect while streaming to indicate ongoing generation. Users perceive streaming responses as 3-5x faster than equivalent non-streaming…
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Building Conversational AI with Azure Bot Service and GPT-4o
Leverage Teams-specific capabilities like adaptive cards, task modules, and message extensions to create rich interactive experiences.