How to Evaluate Ontology Quality: Metrics, Methods, and Tools

This article surveys ontology quality evaluation by outlining key metrics such as consistency, completeness, and coverage, and reviewing five major assessment approaches—including corpus‑based, gold‑standard, metric‑driven, rule‑based, and application‑driven methods—while highlighting representative tools, open‑source implementations, and future research challenges.

Knowledge EngineeringLarge Language Modelsevaluation methods

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How to Evaluate Ontology Quality: Metrics, Methods, and Tools

AI Frontier Lectures

Jun 28, 2025 · Artificial Intelligence

Why Multi-Agent AI Systems Outperform Single Agents: Anthropic’s Research Blueprint

Anthropic’s multi‑agent research system demonstrates how coordinated specialist agents, dynamic prompting, and extensive token usage can dramatically boost performance on open‑ended tasks, while also revealing challenges in cost, evaluation, and production reliability that must be managed for real‑world deployment.

AI research systemsAnthropicMulti-Agent AI

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Why Multi-Agent AI Systems Outperform Single Agents: Anthropic’s Research Blueprint

Data Thinking Notes

Jun 24, 2025 · Artificial Intelligence

Anthropic’s Multi‑Agent Research System: Architecture, Lessons & 90% Performance Boost

Anthropic’s detailed post explains how its new Research feature uses a multi‑agent architecture with a lead coordinator and parallel sub‑agents, covering design principles, prompt engineering tricks, evaluation methods, production reliability challenges, and the substantial performance gains achieved over single‑agent baselines.

AI ArchitectureLLM researchPrompt engineering

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Anthropic’s Multi‑Agent Research System: Architecture, Lessons & 90% Performance Boost

NewBeeNLP

Sep 25, 2024 · Artificial Intelligence

From Zero to One: A Practical Guide to Pretraining Large Language Models

This comprehensive guide walks through every stage of LLM pretraining—from data sourcing, cleaning, and deduplication, to tokenizer design, model architecture choices, training framework selection, optimization tricks, and evaluation methods—offering actionable tips and pitfalls to avoid.

LLM PretrainingTraining Frameworkdata collection

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From Zero to One: A Practical Guide to Pretraining Large Language Models

Sohu Tech Products

Apr 24, 2024 · Artificial Intelligence

Domain-Specific Large Model Construction Guide

The guide explains why generic LLMs struggle with enterprise tasks and outlines two remedies—retrieval‑augmented generation and domain‑specific fine‑tuning—detailing dataset creation, training strategies (full‑parameter, LoRA, Q‑LoRA), validation methods, hardware benchmarks, and practical tips such as supervised fine‑tuning, 30% domain data, and a stepwise tuning pipeline.

AIdataset constructiondomain-specific LLM

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Domain-Specific Large Model Construction Guide

21CTO

Apr 20, 2021 · Fundamentals

Why A/B Testing Isn’t a Silver Bullet: Lessons from ByteDance’s Data‑Driven Journey

In a ByteDance tech open‑day talk, Yang Zhenyuan explains how clear, measurable goals, the limits of user‑profile and usage‑time metrics, and careful A/B test design are essential for data‑driven product decisions, while highlighting practical pitfalls and real‑world examples.

A/B testingData-drivenGoal Setting

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