This page shows the most relevant public items for AI Engineering, ranked by trend activity and review signal. Use weekly for fast changes, monthly for more stable patterns, and all-time for evergreen picks.
Book • Jan 9, 2024 • Amazon • Chris Fregly, Antje Barth, Shelbee Eigenbrode
Cloud infrastructure is the backbone of the modern generative AI boom, and AWS remains the dominant platform for enterprise deployment. This book provides a detailed architectural guide to building...
Book • Oct 15, 2024 • Amazon • Jay Alammar, Maarten Grootendorst
Known for his incredibly clear visual explanations of AI architectures, Jay Alammar teams up with Maarten Grootendorst to provide a deeply practical guide to using modern LLMs. This book focuses he...
Book • Jan 7, 2025 • Amazon • Chip Huyen
As AI transitions from research labs to production environments, the focus has shifted from training models to building robust applications around them. Chip Huyen, a leading voice in ML systems, o...
Book • Apr 1, 2025 • Amazon • Sebastian Raschka
This highly anticipated release takes readers on a deep, code-first journey into the inner workings of foundation models. Author Sebastian Raschka guides developers step-by-step through constructin...
Book • Mar 16, 2026 • Amazon • Martin Kleppmann
Data is at the center of many challenges in system design today. This book provides a comprehensive overview of the algorithms and principles behind modern databases and distributed systems. It bre...
Paper • Mar 9, 2026 • arXiv • J. Smith, A. Doe, L. Weiss
This paper presents a novel AI Engineering curriculum designed to integrate agentic tools directly into the agile software development lifecycle. It advocates for moving beyond simple AI lectures t...
Paper • Mar 7, 2026 • arXiv • Junming Liu, Yuqi Li, Shiping Wen, Zhigang Zeng, Tingwen Huang
Hit-RAG addresses a key challenge in long-context retrieval systems: attention dilution caused by large volumes of retrieved evidence. The framework introduces a multi-stage preference alignment pi...
Paper • Feb 7, 2026 • arXiv • Xingliang Hou, Yuyan Liu, Qi Sun, Haoxiu Wang, Hao Hu, Shaoyi Du, Zhiqiang Tian
IGMiRAG introduces a cognitive-inspired retrieval framework that simulates intuition-driven reasoning over hierarchical knowledge graphs. The system constructs a heterogeneous hypergraph to represe...
Paper • Feb 14, 2026 • arXiv • Rui Yu, Tianyi Wang, Ruixia Liu, Yinglong Wang
PT-RAG proposes a structure-aware retrieval framework designed specifically for long academic documents. Traditional RAG pipelines flatten documents into chunks, which destroys the natural hierarch...
Paper • Nov 20, 2025 • arXiv • Hao Hu, Yifan Feng, Ruoxue Li, Rundong Xue
Cog-RAG introduces a cognitive-inspired dual-hypergraph architecture designed to capture both thematic and entity-level relationships within large document collections. The framework constructs two...
Blog • Dec 22, 2025 • Latent Space • Swyx
This article analyzes how AI agents are reshaping the software industry. It explores the shift from traditional applications toward autonomous systems capable of planning and executing tasks. The a...
Blog • Nov 2, 2025 • Latent Space • Swyx, Alessio Fanelli
This article explores the emerging technology stack required to build AI agents. It analyzes infrastructure components such as orchestration frameworks, evaluation systems, and tool integrations. T...
Blog • Oct 18, 2025 • Chip Huyen Blog • Chip Huyen
This article focuses on the engineering challenges of building reliable AI applications. It explores issues such as evaluation pipelines, monitoring strategies, and system observability. The author...
Blog • Dec 5, 2025 • LangChain Blog • LangChain
This article examines practical workflow patterns used when building AI agents in production systems. It explains how agents coordinate tools, reasoning steps, and external APIs to perform complex ...
Blog • Nov 20, 2025 • Language Models Newsletter • Chip Huyen
This article explores the engineering challenges involved in designing production AI systems. It discusses topics such as system architecture, monitoring, data pipelines, and evaluation. The author...
Blog • Oct 2, 2025 • Lilian Weng Blog • Lilian Weng
This long-form technical article explores the design of autonomous agents built on top of large language models. It explains key architectural components such as planning modules, memory systems, t...
Paper • Feb 28, 2026 • arXiv • Virginia Dignum, Frank Dignum
The rapid popularization of the term 'Agentic AI' has led to semantic confusion, often blurring the lines between simple generative loops and true software agency. This position paper critically ex...
Paper • Feb 28, 2026 • arXiv • Anonymous
Recent advancements in agentic reasoning rely heavily on self-verification loops, where models constantly double-check their own logic. However, this often leads to excessive computational waste an...
Paper • Feb 28, 2026 • arXiv • Rui Li, Zeyu Zhang, Xiaohe Bo, Quanyu Dai, Chaozhuo Li, Feng Wen, Xu Chen
Scaling multi-agent swarms using static, centralized orchestrators frequently leads to communication bottlenecks and single points of failure. This paper approaches agentic coordination through the...
Paper • Mar 2, 2026 • arXiv • Anonymous
Extracting structured event arguments from massive, unstructured document repositories is highly error-prone using single-pass LLMs. This paper proposes a collaborative multi-agent architecture fea...
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Use weekly for fast-moving updates, monthly for stable trend confirmation, and all-time for evergreen references. This guidance is specific to AI Engineering topic page on Attendemia and is written so it still makes sense without reading other sections on the page.
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