InfoQ Homepage Large language models Content on InfoQ
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Accelerating LLM-Driven Developer Productivity at Zoox
Amit Navindgi explains how Zoox built Cortex, an internal AI platform that streamlines the developer lifecycle by moving beyond the hype to deliver secure, agentic workflows and real-world impact.
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Dynamic Moments: Weaving LLMs into Deep Personalization at DoorDash
Sudeep Das and Pradeep Muthukrishnan discuss how DoorDash combines LLMs with deep learning to move from "classic" collaborative filtering to "hyper-personalization" in real-time commerce.
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Building Embedding Models for Large-Scale Real-World Applications
Sahil Dua explains the architecture and training of embedding models. He shares practical tips for distilling large models and scaling RAG applications for real-time production environments.
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Ecologies and Economics of Language AI in Practice
Jade Abbott explains how to build sustainable AI using "Little LMs." She discusses environmental impacts, linguistic justice, and technical optimizations like quantization and model distillation.
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Why Observability Matters (More!) with AI Applications
Sally O'Malley shares how to build an AI observability stack with open-source tools (Prometheus, Grafana, OpenTelemetry, Tempo, vLLM/Llama Stack). Learn to track performance, quality and cost signals.
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Deploy MultiModal RAG Systems with vLLM
Stephen Batifol explains the core concepts of multimodal RAG systems, vector search indexes (HNSW, IVF), and embedding model selection. He details vLLM and Pixtral for optimized inference..
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Chatting with Your Knowledge Graph
Jonathan Lowe discusses how to enable an LLM to chat with a structured graph database. He explains the process of using semantic search and knowledge graphs to answer natural language questions.
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The Data Backbone of LLM Systems
Paul Iusztin discusses the evolution of AI engineering, highlighting the shift from model training to foundational models. He shares insights on scalable LLM systems and optimizing RAG.
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Enhance LLMs’ Explainability and Trustworthiness with Knowledge Graphs
Leann Chen discusses how knowledge graphs provide structured data to enhance LLM accuracy, tackling common challenges like hallucinations and the "lost-in-the-middle" phenomenon in RAG systems.
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AI Agents & LLMs: Scaling the Next Wave of Automation
The panelists discuss AI agents and LLMs, exploring their definitions, architectures, use cases, reliability, and impact on the SDLC and future of automation.
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A Framework for Building Micro Metrics for LLM System Evaluation
Denys Linkov discusses critical lessons for senior developers and leaders on building robust LLM systems and actionable metrics that prevent production issues and drive business value.
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Scaling Large Language Model Serving Infrastructure at Meta
Ye (Charlotte) Qi explains key considerations for optimizing LLM inference, including hardware, latency, and production scaling strategies.
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