Introduction: Semantic Kernel is Microsoft’s open-source SDK for integrating Large Language Models into applications. Originally developed to power Microsoft 365 Copilot, it has evolved into a comprehensive framework for building AI-powered applications with enterprise-grade features. Unlike other LLM frameworks that focus primarily on Python, Semantic Kernel provides first-class support for both C# and Python, making […]
Read more →Category: Technology Engineering
Technology Engineering
Multimodal AI Applications: Building Systems That See, Hear, and Understand
Introduction: Multimodal AI processes and generates content across multiple modalities—text, images, audio, and video. This capability enables applications that were previously impossible: describing images, generating images from text, transcribing and understanding audio, and creating unified experiences that combine all these modalities. This guide covers the practical aspects of building multimodal applications: vision-language models for image […]
Read more →An Introduction to DevSecOps: Unlocking Success with Real-World Examples
Introduction In today’s fast-paced world, the need for rapid and secure software development has never been more crucial. As organizations strive to meet these demands, the DevSecOps approach has emerged as a powerful solution that integrates security practices into the DevOps process. By combining development, security, and operations, DevSecOps enables teams to create high-quality, secure […]
Read more →Embedding Models Compared: OpenAI vs Cohere vs Voyage vs Open Source
Introduction: Embedding models convert text into dense vectors that capture semantic meaning. Choosing the right embedding model significantly impacts search quality, retrieval accuracy, and application performance. This guide compares leading embedding models—OpenAI’s text-embedding-3, Cohere’s embed-v3, Voyage AI, and open-source alternatives like BGE and E5. We cover benchmarks, pricing, dimension trade-offs, and practical guidance on selecting […]
Read more →RAG Optimization: Query Rewriting, Hybrid Search, and Re-ranking
Introduction: Retrieval-Augmented Generation (RAG) grounds LLM responses in factual data, but naive implementations often retrieve irrelevant content or miss important information. Optimizing RAG requires attention to every stage: query understanding, retrieval strategies, re-ranking, and context integration. This guide covers practical optimization techniques: query rewriting and expansion, hybrid search combining dense and sparse retrieval, re-ranking with […]
Read more →LLM Routing and Model Selection: Optimizing Cost and Quality in Production
Introduction: Not every query needs GPT-4. Routing simple questions to cheaper, faster models while reserving expensive models for complex tasks can cut costs by 70% or more without sacrificing quality. Smart LLM routing is the difference between a $10,000/month AI bill and a $3,000 one. This guide covers implementing intelligent model selection: classifying query complexity, […]
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