OpenAI and Microsoft have released GPT-5.2-Codex—the latest evolution of the Codex line specifically optimized for software development. With a 400,000 token context window, support for 50+ programming languages, and multimodal capabilities that process code, natural language, images, and diagrams simultaneously, Codex 5.2 represents a step-change in AI-assisted development. Available through Azure AI Foundry and GitHub […]
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NVIDIA Dynamo Planner: LLM Inference Optimization on Azure Kubernetes Service
In January 2026, Microsoft and NVIDIA released the second iteration of the NVIDIA Dynamo Planner—a groundbreaking tool for optimizing large language model (LLM) inference on Azure Kubernetes Service (AKS). This collaboration addresses one of the most challenging aspects of production AI: efficiently scaling GPU resources to balance cost, latency, and throughput. This comprehensive guide explores […]
Read more →Semantic Search in Production: Embedding Strategies for Enterprise RAG
The quality of your RAG (Retrieval-Augmented Generation) system depends more on your embedding strategy than on your choice of LLM. Poor embeddings mean irrelevant context retrieval, which no amount of prompt engineering can fix. This comprehensive guide explores production-ready embedding strategies—covering model selection, chunking approaches, hybrid search techniques, and optimization patterns that directly impact retrieval […]
Read more →From RAG to Agents: The Evolution of AI Applications in 2025
From RAG to Agents: The Evolution of AI Applications in 2025 A Comprehensive Analysis of How AI Applications Evolved from Retrieval-Augmented Generation to Autonomous Agent Systems December 2025 | Industry Whitepaper Retrieval-Augmented Generation (RAG) revolutionized how we build LLM applications by grounding responses in real data. But RAG has limitations: it’s reactive, constrained to retrieval […]
Read more →AI Engineering in 2025: The Year That Changed Everything – A Comprehensive Review
A comprehensive review of the most transformative year in AI engineering history. From GPT-5.2 to Gemini 3, xAI’s Grok 4, DeepSeek’s rise, Kimi K2’s emergence, regulatory shifts, and what’s coming in 2026.
Read more →Getting Started with Full Stack AI Engineering: A Practical Guide for 2026
A comprehensive guide to becoming a Full Stack AI Engineer in 2026. Learn the complete stack from frontend to infrastructure, with practical code examples using GPT-5, Python, FastAPI, LangChain, and Next.js for building AI-powered applications.
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