technical guide
30 articles about technical guide in AI news
Azure ML Workspace with Terraform: A Technical Guide to Infrastructure-as-Code for ML Platforms
The source is a technical tutorial on Medium explaining how to deploy an Azure Machine Learning workspace—the central hub for experiments, models, and pipelines—using Terraform for infrastructure-as-code. This matters for teams seeking consistent, version-controlled, and automated cloud ML infrastructure.
A Technical Guide to Prompt and Context Engineering for LLM Applications
A Korean-language Medium article explores the fundamentals of prompt engineering and context engineering, positioning them as critical for defining an LLM's role and output. It serves as a foundational primer for practitioners building reliable AI applications.
AWS Bedrock Agents vs. AgentCore: A Technical Guide for AI Architects
AWS offers two distinct approaches for building AI agents: the fully managed Bedrock Agents for speed and the low-level AgentCore framework for control. This article breaks down the architectural differences, code examples, and selection criteria for production deployments.
Agent Washing vs. Real Agents: A Production Engineer's Guide to Telling the Difference
A technical guide exposes 'agent washing'—where chatbots and automation scripts are rebranded as AI agents—and provides a 5-point checklist to identify genuinely agentic systems that can survive production. This matters because 88% of AI agents never reach production.
Fine-Tuning OpenAI's GPT-OSS 20B: A Practitioner's Guide to LoRA on MoE Models
A technical guide details the practical challenges and solutions for fine-tuning OpenAI's 20-billion parameter GPT-OSS model using LoRA. This is crucial for efficiently adapting large, complex MoE models to specific business domains.
LLM Fine-Tuning Explained: A Technical Primer on LoRA, QLoRA, and When to Use Them
A technical guide explains the fundamentals of fine-tuning large language models, detailing when it's necessary, how the parameter-efficient LoRA method works, and why the QLoRA innovation made the process dramatically more accessible.
A/B Testing RAG Pipelines: A Practical Guide to Measuring Chunk Size, Retrieval, Embeddings, and Prompts
A technical guide details a framework for statistically rigorous A/B testing of RAG pipeline components—like chunk size and embeddings—using local tools like Ollama. This matters for AI teams needing to validate that performance improvements are real, not noise.
Agent Harnessing: The Infrastructure That Makes AI Agents Work
A detailed technical guide argues that the model is not the hard part of building AI agents. The six-component harness — context management, memory, tools, control flow, verification, and coordination — is what separates production-grade agents from those that fail silently.
Harness Engineering for AI Agents: Building Production-Ready Systems That Don’t Break
A technical guide on 'Harness Engineering'—a systematic approach to building reliable, production-ready AI agents that move beyond impressive demos. This addresses the critical industry gap where most agent pilots fail to reach deployment.
When to Prompt, RAG, or Fine-Tune: A Practical Decision Framework for LLM Customization
A technical guide published on Medium provides a clear decision framework for choosing between prompt engineering, Retrieval-Augmented Generation (RAG), and fine-tuning when customizing LLMs for specific applications. This addresses a common practical challenge in enterprise AI deployment.
Modern RAG in 2026: A Production-First Breakdown of the Evolving Stack
A technical guide outlines the critical components of a modern Retrieval-Augmented Generation (RAG) system for 2026, focusing on production-ready elements like ingestion, parsing, retrieval, and reranking. This matters as RAG is the dominant method for grounding enterprise LLMs in private data.
Building a Next-Generation Recommendation System with AI Agents, RAG, and Machine Learning
A technical guide outlines a hybrid architecture for recommendation systems that combines AI agents for reasoning, RAG for context, and traditional ML for prediction. This represents an evolution beyond basic collaborative filtering toward systems that understand user intent and context.
Fine-Tuning Llama 3 with Direct Preference Optimization (DPO): A Code-First Walkthrough
A technical guide details the end-to-end process of fine-tuning Meta's Llama 3 using Direct Preference Optimization (DPO), from raw preference data to a deployment-ready model. This provides a practical blueprint for customizing LLM behavior.
Fine-Tuning Strategies for AI Agents on Azure: Balancing Accuracy, Cost, and Performance
A technical guide explores strategies for fine-tuning AI agents on Microsoft Azure, focusing on the critical trade-offs between model accuracy, operational cost, and system performance. This is essential for teams deploying autonomous AI systems in production environments.
A Deep Dive into LoRA: The Mathematics, Architecture, and Deployment of Low-Rank Adaptation
A technical guide explores the mathematical foundations, memory architecture, and structural consequences of Low-Rank Adaptation (LoRA) for fine-tuning LLMs. It provides critical insights for practitioners implementing efficient model customization.
Building ReAct Agents from Scratch: A Deep Dive into Agentic Architectures, Memory, and Guardrails
A comprehensive technical guide explains how to construct and secure AI agents using the ReAct (Reasoning + Acting) framework. This matters for retail AI leaders as autonomous agents move from theory to production, enabling complex, multi-step workflows.
Building Semantic Product Recommendation Systems with Two-Tower Embeddings
A technical guide explains how to implement a two-tower neural network architecture for product recommendations, creating separate embeddings for users and items to power similarity search and personalized ads. This approach moves beyond simple collaborative filtering to semantic understanding.
Efficient Fine-Tuning of Vision-Language Models with LoRA & Quantization
A technical guide details methods for fine-tuning large VLMs like GPT-4V and LLaVA using Low-Rank Adaptation (LoRA) and quantization. This reduces computational cost and memory footprint, making custom VLM training more accessible.
LLM-as-a-Judge: A Practical Framework for Evaluating AI-Extracted Invoice Data
A technical guide demonstrating how to use LLMs as evaluators to assess the accuracy of AI-extracted invoice data, replacing manual checks and brittle validation rules with scalable, structured assessment.
Fine-Tuning Gemma 3 1B-IT for Financial Reasoning with QLoRA
A technical guide details using QLoRA and reasoning-augmented data to fine-tune Google's Gemma 3 1B-IT model for financial analysis. This demonstrates a method to specialize small language models for complex, domain-specific tasks.
Multi-Agent AI Systems: Architecture Patterns and Governance for Enterprise Deployment
A technical guide outlines four primary architecture patterns for multi-agent AI systems and proposes a three-layer governance framework. This provides a structured approach for enterprises scaling AI agents across complex operations.
NVIDIA and Cisco Publish Practical Guide for Fine-Tuning Enterprise Embedding Models
Cisco Blogs published a guide detailing how to fine-tune embedding models for enterprise retrieval using NVIDIA's Nemotron recipe. This provides a technical blueprint for improving domain-specific search and RAG systems, a critical component for AI-powered enterprise applications.
VMLOps Publishes NLP Engineer System Design Interview Guide
VMLOps has published 'The NLP Engineer's System Design Interview Guide,' a detailed resource covering architecture, scaling, and trade-offs for real-world NLP systems. It provides a structured framework for both interviewers and candidates.
NATO Tests SWARM Biotactics' AI-Guided Cyborg Cockroaches for Recon
NATO is evaluating a biohybrid system from German defense startup SWARM Biotactics, which uses AI to guide live cockroaches fitted with sensor backpacks through complex environments for military reconnaissance.
Anthropic's Claude Code vs. OpenClaw: A Technical Comparison
A technical dive compares Anthropic's Claude Code, a specialized coding model, against the open-source OpenClaw. The analysis examines benchmarks, capabilities, and the trade-offs between proprietary and open-source AI for code.
Binghamton University Tests Robotic Guide Dog with Natural Language Interface
Researchers at Binghamton University have developed a robotic guide dog prototype that communicates with users using natural language. The system, built on a Unitree Go2 platform, was demonstrated navigating a user through a test environment.
Entropy-Guided Branching Boosts Agent Success 15% on New SLATE E-commerce
A new paper introduces SLATE, a large-scale benchmark for evaluating tool-using AI agents, and Entropy-Guided Branching (EGB), an algorithm that improves task success rates by 15% by dynamically expanding search where the model is uncertain.
BoF Launches 'The Fashion Marketer's Guide to AI' Masterclass
The Business of Fashion (BoF) has announced a new professional masterclass titled 'The Fashion Marketer's Guide to AI.' This indicates a formalized educational push to equip fashion industry professionals with actionable AI knowledge.
A Practical Guide to Fine-Tuning an LLM on RunPod H100 GPUs with QLoRA
The source is a technical tutorial on using QLoRA for parameter-efficient fine-tuning of an LLM, leveraging RunPod's cloud H100 GPUs. It focuses on the practical setup and execution steps for engineers.
Technical Implementation: Building a Local Fine-Tuning Engine with MLX
A developer shares a backend implementation guide for automating the fine-tuning process of AI models using Apple's MLX framework. This enables private, on-device model customization without cloud dependencies, which is crucial for handling sensitive data.