InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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Where Architects Sit in the Era of AI
As AI evolves from tool to collaborator, architects must shift from manual design to meta-design. This article introduces the "Three Loops" framework (In, On, Out) to help navigate this transition. It explores how to balance oversight with delegation, mitigate risks like skill atrophy, and design the governance structures that keep AI-augmented systems safe and aligned with human intent.
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Architecture in a Flow of AI-Augmented Change
While AI adoption is surging, most organizations fail to scale past pilots. The solution lies in organizational structure, not just technology. This article details how architects can enable "fast flow" by defining clear domains and guardrails. Learn how to shift from controlling outcomes to curating context, allowing AI to drive continuous, valuable business change.
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NextGen Search - Where AI Meets OpenSearch Through MCP
In this article, authors Srikanth Daggumalli and Arun Lakshmanan discuss next-generation context-aware conversational search using OpenSearch and AI agents powered by Large Language Models (LLMs) and Model Context Protocol (MCP).
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Reducing False Positives in Retrieval-Augmented Generation (RAG) Semantic Caching: a Banking Case Study
In this article, author Elakkiya Daivam discusses why Retrieval Augmented Generation (RAG) and semantic caching techniques are powerful levers for reducing false positives in AI powered applications. She shares the insights from a production-grade evaluation with 1,000 query variations tested across seven bi-encoder models.
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Training Data Preprocessing for Text-to-Video Models
In this article, author Aleksandr Rezanov discusses the data preparation for generative text-to-image models to accelerate work on video generation services to be used in TV series and films. He explains how data is prepared and can serve as a starting point for creating custom datasets to develop proprietary models.
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A Plan-Do-Check-Act Framework for AI Code Generation
AI code generation tools promise faster development but often create quality issues, integration problems, and delivery delays. A structured Plan-Do-Check-Act cycle can maintain code quality while leveraging AI capabilities. Through working agreements, structured prompts, and continuous retrospection, it asserts accountability over code while guiding AI to produce tested, maintainable software.
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Disaggregation in Large Language Models: the Next Evolution in AI Infrastructure
Large Language Model (LLM) inference faces a fundamental challenge: the same hardware that excels at processing input prompts struggles with generating responses, and vice versa. Disaggregated serving architectures solve this by separating these distinct computational phases, delivering throughput improvements and better resource utilization while reducing costs.
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InfoQ AI, ML and Data Engineering Trends Report - 2025
This InfoQ Trends Report offers readers a comprehensive overview of emerging trends and technologies in the areas of AI, ML, and Data Engineering. This report summarizes the InfoQ editorial team’s and external guests' view on the current trends in AI and ML technologies and what to look out for in the next 12 months.
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Effective Practices for Architecting a RAG Pipeline
Hybrid search, smart chunking, and domain-aware indexing are key to building effective RAG pipelines. Context window limits and prompt quality critically affect LLM response accuracy. This article provides lessons learned from setting up a RAG pipeline.
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How Causal Reasoning Addresses the Limitations of LLMs in Observability
Large language models excel at converting observability telemetry into clear summaries but struggle with accurate root cause analysis in distributed systems. LLMs often hallucinate explanations and confuse symptoms with causes. This article suggests how causal reasoning models with Bayesian inference offer more reliable incident diagnosis.
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MCP: the Universal Connector for Building Smarter, Modular AI Agents
In this article, the authors discuss Model Context Protocol (MCP), an open standard designed to connect AI agents with tools and data they need. They also talk about how MCP empowers agent development, and its adoption in leading open-source frameworks.
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The Missing Layer in AI Infrastructure: Aggregating Agentic Traffic
In this article, author Eyal Solomon discusses AI Gateways, the outbound proxy servers that intercept and manage AI-agent-initiated traffic in real time to enforce policies and provide central management.