AI engineering article archive

Explore guides and articles on AI development, retrieval systems, and production engineering. Page 11 of 11.

• Vector Database Comparison: Pinecone, Weaviate, Qdrant & Milvus

  Compare vector database deployment models, hybrid retrieval, cost assumptions, and production evaluation methods for RAG applications.

• RAG (Retrieval Augmented Generation): Complete Implementation Guide 2025

  Master RAG systems: vector databases, embeddings, retrieval strategies, and production implementation. Complete guide with code examples and case studies.

• LLM Fine-Tuning Guide: LoRA, QLoRA, Data & Evaluation

  Plan an LLM fine-tuning experiment with clean training data, LoRA or QLoRA, held-out evaluation, reproducible artifacts and measured serving costs.

• AI for Retail: Complete Guide to E-commerce AI & Personalization 2025

  Transform retail with AI-powered personalization, demand forecasting, dynamic pricing, and computer vision. Complete implementation guide with case studies and ROI analysis.

• Healthcare AI Development: Validation, Data Controls & EHR Integration

  Plan healthcare AI around intended use, representative validation, patient-data controls, EHR integration, human review, and monitored rollout.

• Understanding Swift Concurrency: Actors, Executors, and Reentrancy

  An engineering-focused deep dive into Swift concurrency, covering actors, nonisolated methods, MainActor and GlobalActor boundaries, and the runtime concepts behind jobs, executors, workers, and schedulers. Designed for TensorBlue readers, the post explains how these mechanisms impact safety, performance, and product design, and what teams must implement to ship reliable, responsive software.

• Java News Roundup May 2026: JEPs for JDK 27, Azul Payara Community, WildFly, LangChain4j, OpenXava, and Google ADK

  A technical look at the May 11, 2026 Java roundup: four JDK 27 JEPs advancing through review, new tooling from Azul Payara and WildFly, and maintenance releases across Micronaut, LangChain4j, OpenXava, and Google ADK. The post unpacks what these updates mean for performance, security, deployment, and AI-enabled Java workloads.

• Beyond the Chatbot: Engineering a Real-World GitHub Auditor in TypeScript

  A practical blueprint for building an intelligent GitHub auditing tool in TypeScript that goes beyond simple automation. This post translates high-level ideas from a DZone piece into a real-world architecture, focusing on triage, policy enforcement, and actionable guidance while acknowledging the source's partial nature.

• Elastic 9.3.0 Elevates AI Workflows with GPU-Accelerated Vector Indexing, ES|QL Upgrades, and OpenTelemetry Support

  Elastic 9.3.0 expands AI-driven search by accelerating vector indexing with cuVS, upgrading the ES|QL language, and deepening OpenTelemetry integration. The release targets production RAG workloads across hybrid environments, offering performance gains, on-engine analytics, and unified telemetry without vendor lock-in.

• Bloomberg House to Convene Influential Global Leaders at Davos, Switzerland

  An exclusive gathering of thought leaders discussing the most pressing challenges and opportunities facing the global economy.

• Representation Will Define the Future of Creativity More Than Technology

  Why diverse representation in creative fields matters more than technological advancement for the future of innovation.

• Composable AI Architecture: Routing, Contracts & Tradeoffs

  Evaluate composable AI with explicit component contracts, routing tests, version compatibility, failure handling, and end-to-end quality, latency, and cost measurements.

• AI for Scientific Discovery: Evidence & Research Workflows

  Understand how AI supports scientific discovery through structure prediction and automated experiments, with practical checks for evidence, data quality, and reproducibility.

• AI Agents: Tool Use, Permissions & Evaluation

  Design AI agents around bounded tasks, explicit tool permissions, persisted state, stopping conditions, and evaluation of actions as well as final answers.

• AI Agent Development: From Prototype to Reviewed Production

  Build an AI agent around a task contract, narrow tools, durable execution state, failure tests and a measured release gate.