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Structured Outputs: Getting Reliable Data from LLMs
Structured outputs enable reliable integration of LLM capabilities into data pipelines.
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Thoughts on data engineering, cloud architecture, and technology. 2205 articles and counting.
Showing 24 of 2205 posts — page 20 of 92
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Function Calling Patterns: Building Reliable AI Tools
Well-designed function calling creates powerful AI applications that interact safely with real systems.
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Prompt Caching Strategies: Reducing Latency and Cost
Smart caching can reduce AI costs by 30-50% for applications with repetitive queries.
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Context Window Management: Maximizing LLM Effectiveness
Strategic context management enables handling of complex, long-context scenarios.
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Query Transformation Techniques: Improving RAG Recall
Query transformation is a high-leverage improvement for RAG systems.
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Reranking Strategies: Improving RAG Precision
Cross-encoder reranking typically improves RAG precision by 10-20%.
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Embedding Models Comparison: Choosing the Right Model for Your Use Case
Balance quality, speed, and cost based on your specific retrieval needs.
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Chunking Strategies for RAG: Finding the Right Granularity
Choose chunking strategy based on document structure and retrieval requirements.
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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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Microsoft Fabric Copilot Now Available for All Paid SKUs
This works on F2 and above now. For small businesses, this transforms how reports are built.
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Semantic Layer for AI: Building Business-Ready Data Interfaces
A semantic layer enables AI to understand business terminology and generate accurate queries.
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AI-Powered Documentation: Keeping Docs in Sync with Code
AI-generated documentation reduces maintenance burden while improving quality.
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Testing AI Applications: Strategies for Non-Deterministic Systems
Robust AI testing combines deterministic checks with AI-powered evaluation.
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Data Quality Automation with AI: Beyond Rule-Based Validation
AI-powered data quality catches issues that traditional rules miss.
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dbt and AI: Intelligent Data Transformations
AI supercharges dbt workflows with intelligent generation and optimization.
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AI-Assisted Data Modeling: Accelerating Schema Design
AI-assisted modeling reduces design time while improving schema quality.
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Data Mesh and AI: Decentralized Intelligence at Scale
Data mesh with AI enables domain teams to own both data and intelligence.
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GraphRAG Implementation: Combining Graphs with Retrieval
GraphRAG excels at complex questions requiring synthesis across multiple documents.
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Knowledge Graphs and AI: Building Intelligent Data Structures
Knowledge graphs add structured reasoning capabilities to AI applications.
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RAG Advanced Patterns: Beyond Basic Retrieval
Advanced RAG combines query expansion, hybrid retrieval, reranking, and context compression for better results.
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February 2025 AI Recap: Key Developments and Trends
NPUs becoming standard in new PCs Phi 3 and similar SLMs enabling local inference Windows AI features expanding Vector + keyword + sparse becoming standard…
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AI Orchestration Frameworks: Building Complex AI Workflows
Choose orchestration frameworks based on workflow complexity and team expertise.
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LangChain vs Semantic Kernel: Choosing Your AI Framework
Choose based on your ecosystem and team skills.
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Semantic Kernel Patterns: Building AI Applications with Microsoft's SDK
Semantic Kernel provides a clean abstraction for building AI applications with plugins and natural language orchestration.