BestHub
Sign in
Tagged articles

Evaluation

213 articles · Page 1 of 3
Linyb Geek Road
Linyb Geek Road
Jul 4, 2026 · Artificial Intelligence

Iterative Development of Agent Skills: A Hands‑On Guide

This article explains the concept of Agent Skill as a modular, file‑system‑based knowledge asset for AI agents, outlines its three‑layer progressive disclosure architecture, details suitable and unsuitable scenarios, and provides concrete iterative development practices—including decision‑tree design, dual verification, and tool‑supported workflows—to turn expert expertise into reusable, zero‑dependency SOPs.

AI workflowAgent SkillDecision Tree
0 likes · 16 min read
Iterative Development of Agent Skills: A Hands‑On Guide
dbaplus Community
dbaplus Community
Jun 30, 2026 · Artificial Intelligence

Designing a Production-Grade Multi-Agent Harness: Architecture, Evaluation, Memory, Cost, and MCP Integration

This article dissects the essential components of a production‑ready Multi‑Agent Harness—its orchestration architecture, tool governance via a unified registry, layered state and memory management, comprehensive evaluation pipelines, token‑budget cost controls, MCP‑based tool integration, observability practices, and a phased roadmap for scaling, offering concrete guidelines and best‑practice recommendations for building reliable AI agent systems.

Cost ControlEvaluationHarness
0 likes · 18 min read
Designing a Production-Grade Multi-Agent Harness: Architecture, Evaluation, Memory, Cost, and MCP Integration
Data Party THU
Data Party THU
Jun 30, 2026 · Artificial Intelligence

Do Video Generation Models Really Reason? A 303‑Question Benchmark Exposes Their Reasoning Gaps

The article introduces the MME‑CoF‑Pro benchmark, which uses 303 carefully crafted video‑reasoning samples across 16 categories to evaluate seven leading video generation models, revealing that current models lack true reasoning ability, that prompting can both help and hurt coherence, and that the new Reasoning Score aligns well with human judgments.

EvaluationMME-CoF-Proartificial-intelligence
0 likes · 11 min read
Do Video Generation Models Really Reason? A 303‑Question Benchmark Exposes Their Reasoning Gaps
Data Party THU
Data Party THU
Jun 29, 2026 · Artificial Intelligence

Mapping LLM Reasoning: Paradigms, Methods, and Failure Modes in a Periodic Table

This 103‑page survey of over 300 recent papers organizes large language model reasoning into a periodic‑table framework, explains where reasoning emerges, categorizes 36 method families across six dimensions, critiques accuracy‑only evaluation, and outlines key open challenges such as fidelity, robustness, calibration, generalization, efficiency, and safety.

AI safetyChain-of-ThoughtEvaluation
0 likes · 13 min read
Mapping LLM Reasoning: Paradigms, Methods, and Failure Modes in a Periodic Table
AI Engineer Programming
AI Engineer Programming
Jun 29, 2026 · Artificial Intelligence

Managing LLM Hallucinations: Strategies, Metrics, and Layered Controls

The article examines why large language models hallucinate, categorizes factual, faithfulness, and reasoning hallucinations, critiques existing benchmarks, and proposes a layered governance framework—including training‑time RLHF/DPO, retrieval‑augmented generation, post‑generation verification, uncertainty quantification, and compliance considerations—to mitigate risks in production systems.

EvaluationHallucinationLLM
0 likes · 13 min read
Managing LLM Hallucinations: Strategies, Metrics, and Layered Controls
Linyb Geek Road
Linyb Geek Road
Jun 28, 2026 · Artificial Intelligence

12 Pitfalls I Learned While Building AI Skills Over Six Months

Over the past half‑year the author built dozens of AI Skills, discovering twelve common traps—from over‑relying on prompts and bloated skill sets to vague descriptions, hidden token costs, knowledge placement, security gaps, and the need for proper evaluation—offering concrete guidance to avoid them.

AI SkillsAgentEvaluation
0 likes · 11 min read
12 Pitfalls I Learned While Building AI Skills Over Six Months
Data Party THU
Data Party THU
Jun 27, 2026 · Artificial Intelligence

Defining a Good Answer in the Agent Era: A Rubrics Survey

This survey examines how rubrics—structured, multi‑dimensional evaluation criteria—are defined, constructed, and applied to train and evaluate large language models, especially for open‑ended, high‑risk and agentic tasks, while highlighting current challenges such as reward hacking and bias.

AI safetyAgentEvaluation
0 likes · 15 min read
Defining a Good Answer in the Agent Era: A Rubrics Survey
Shuge Unlimited
Shuge Unlimited
Jun 24, 2026 · Artificial Intelligence

Why Every “Don’t” in Your Prompt Might Be Counterproductive – Insights from 25 Superpowers 6.0 Experiments

Analyzing 25 micro‑tests from Superpowers 6.0, the author shows that adding “don’t” clauses often backfires, explains a low‑cost $0.15 per‑sample evaluation loop, presents five empirical laws and two hard rules for prompt wording, and offers a reusable framework for validating your own AI agent prompts.

AI AgentsAnthropicEvaluation
0 likes · 23 min read
Why Every “Don’t” in Your Prompt Might Be Counterproductive – Insights from 25 Superpowers 6.0 Experiments
Linyb Geek Road
Linyb Geek Road
Jun 24, 2026 · Artificial Intelligence

Google Agent Skills Whitepaper: How Lightweight SKILL.md Files Transform AI Agent Development

The whitepaper explains how the SKILL.md‑based agent‑skill framework solves four major LLM pain points—prompt bloat, missing procedural memory, costly multi‑agent ops, and cross‑vendor migration—by introducing a three‑stage progressive loading mechanism, rigorous evaluation standards, and meta‑skill automation for scalable, low‑token AI agents.

AGENTS.mdAgent SkillsEvaluation
0 likes · 35 min read
Google Agent Skills Whitepaper: How Lightweight SKILL.md Files Transform AI Agent Development
Ops Community
Ops Community
Jun 23, 2026 · Artificial Intelligence

Advanced LlamaIndex Indexing, Routing, and Multimodal RAG: A Practical Guide

This article walks through a real‑world contract‑review RAG project, diagnosing low recall, redesigning the system with multiple indexes, a RouterQueryEngine, re‑ranking, knowledge‑graph integration, multimodal support, incremental updates, and a rigorous evaluation framework that boosted recall from 60 % to 92 %.

EvaluationIndexingKnowledge Graph
0 likes · 22 min read
Advanced LlamaIndex Indexing, Routing, and Multimodal RAG: A Practical Guide
Machine Heart
Machine Heart
Jun 22, 2026 · Artificial Intelligence

Building the First Real‑World CLI Workflow Benchmark from 80K Human Terminal Recordings

TerminalWorld leverages over 80,000 developer‑recorded terminal sessions to automatically generate 1,530 verified CLI tasks across 18 workflow categories, and its evaluation of leading LLMs and agent frameworks reveals modest success rates, capability gaps, and the shortcomings of expert‑crafted benchmarks.

AI AgentsEvaluationasciinema
0 likes · 13 min read
Building the First Real‑World CLI Workflow Benchmark from 80K Human Terminal Recordings
MaGe Linux Operations
MaGe Linux Operations
Jun 21, 2026 · Artificial Intelligence

Advanced LlamaIndex Indexing, Routing, and Multimodal RAG Strategies

The article walks through a real‑world legal‑contract RAG project that stalled at 60% recall, diagnoses five root causes, and demonstrates how combining multiple LlamaIndex indexes, a Router, fusion retrieval, re‑ranking, knowledge‑graph and multimodal support raises recall to 92% while outlining evaluation metrics, latency trade‑offs, and practical deployment checklists.

EvaluationIndexingKnowledgeGraph
0 likes · 23 min read
Advanced LlamaIndex Indexing, Routing, and Multimodal RAG Strategies
Alibaba Cloud Native
Alibaba Cloud Native
Jun 18, 2026 · Artificial Intelligence

How Enterprise Agents Can Keep Getting Smarter: Inside Alibaba Cloud’s AgentLoop

The article analyzes the challenges of building a self‑evolving enterprise agent—data collection, dataset construction, multi‑level evaluation, and asset consolidation—and explains how Alibaba Cloud’s AgentLoop addresses each step with full‑stack observation, ontology‑driven pipelines, standardized judges, and memory/experience libraries to close the evolution loop.

AI AgentsAgentLoopEvaluation
0 likes · 14 min read
How Enterprise Agents Can Keep Getting Smarter: Inside Alibaba Cloud’s AgentLoop
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Jun 17, 2026 · Artificial Intelligence

Is More Chain‑of‑Thought Always Better? Introducing E‑GRM for On‑Demand LLM Reasoning

The article critically examines the assumption that longer chain‑of‑thought reasoning always improves large language model performance, presents the E‑GRM framework that dynamically decides when to invoke full CoT based on model‑internal uncertainty, and validates its efficiency and accuracy gains through extensive experiments and ablations.

Ablation StudyChain-of-ThoughtDynamic Routing
0 likes · 16 min read
Is More Chain‑of‑Thought Always Better? Introducing E‑GRM for On‑Demand LLM Reasoning
Weekly Large Model Application
Weekly Large Model Application
Jun 16, 2026 · Artificial Intelligence

Building a Reproducible, Scalable ASR Evaluation Framework for 2025‑2026

The article outlines why a unified ASR evaluation pipeline—combining a TestSet Zoo, Model Zoo, and standardized Benchmark Pipeline—is essential for fair cross‑model comparison, describes 2025‑2026 trends such as multi‑track metrics and robustness, and provides a step‑by‑step implementation guide with best‑practice warnings.

ASREvaluationNeMo
0 likes · 9 min read
Building a Reproducible, Scalable ASR Evaluation Framework for 2025‑2026
Alibaba Cloud Developer
Alibaba Cloud Developer
Jun 16, 2026 · Artificial Intelligence

AI Coding Needs Discipline: My Two‑Month Harness Framework Experience

The article analyzes why the bottleneck in AI‑assisted coding has shifted from model capability to workflow stability, introduces a three‑layer "harness" framework that externalizes discipline, details its evolution through four development phases, and presents a deterministic evaluation platform that quantifies the framework’s effectiveness.

AIAgentEvaluation
0 likes · 27 min read
AI Coding Needs Discipline: My Two‑Month Harness Framework Experience
AI Architecture Hub
AI Architecture Hub
Jun 16, 2026 · Artificial Intelligence

Designing Autonomous Long‑Running Coding Agents: Goals, Evaluators, Loops, and Visual Controls

The article explains how autonomous coding agents are evolving from prompt engineering to comprehensive control systems by defining contract‑style goals, integrating evaluators, implementing loop mechanisms, and visualizing work products, enabling agents to operate reliably over extended engineering cycles without continuous human input.

AI EngineeringAutonomous AgentsClaude Code
0 likes · 13 min read
Designing Autonomous Long‑Running Coding Agents: Goals, Evaluators, Loops, and Visual Controls
Frontend AI Walk
Frontend AI Walk
Jun 14, 2026 · R&D Management

Master the FDE Mindset: Frame‑Do‑Evaluate for Engineer Career Growth

The article introduces the Frame‑Do‑Evaluate (FDE) capability framework, explains why engineers should shift from pure execution to problem definition, process integration, and result closure, and provides concrete steps, self‑assessment questions, and strategies to overcome organizational and personal obstacles for career advancement.

EvaluationFDEFrame-Do-Evaluate
0 likes · 17 min read
Master the FDE Mindset: Frame‑Do‑Evaluate for Engineer Career Growth
James' Growth Diary
James' Growth Diary
Jun 12, 2026 · Artificial Intelligence

Engineering Evaluation and Lifecycle Management for Smarter AI Skills

This guide explains how to use the Skill Creator tool to generate automated trigger tests, compare skill‑enabled versus baseline performance, continuously evaluate results, apply checklists, debug with a six‑step process, avoid six common anti‑patterns, and manage skill versioning and reuse so that AI skills become progressively smarter.

AI SkillAnti-patternsAutomation
0 likes · 21 min read
Engineering Evaluation and Lifecycle Management for Smarter AI Skills
PMTalk Product Manager Community
PMTalk Product Manager Community
Jun 12, 2026 · Product Management

Why AI Product Managers Have Stopped Drawing Prototypes

The article explains how AI product managers have shifted from creating prototype mock‑ups to designing continuous evaluation "exams", building test suites, analyzing data and model behavior, and coordinating cross‑functional teams to turn "usable" AI into truly "good" AI experiences.

AI lifecycleAI product managementEvaluation
0 likes · 10 min read
Why AI Product Managers Have Stopped Drawing Prototypes
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Jun 10, 2026 · Artificial Intelligence

Why Code Is the Core of Agent Harness: Deep Insights from UIUC, Meta, and Stanford

The article explains how code serves as the executable, inspectable, and stateful medium that links reasoning, action, feedback, verification, and collaboration in long‑term AI agents, detailing the harness interface, planning‑execute‑verify loop, multi‑agent coordination, and open research challenges.

AI AgentAgent HarnessCode as Interface
0 likes · 14 min read
Why Code Is the Core of Agent Harness: Deep Insights from UIUC, Meta, and Stanford
PaperAgent
PaperAgent
Jun 9, 2026 · Artificial Intelligence

Defining Standard Answers for Agent‑Era LLMs: A Rubrics Survey

The survey from RUC‑Gaoling AI Institute reviews Rubrics for large language models, explaining why they are needed for open‑ended, high‑risk tasks, how they are constructed, and how they can be applied to policy and reward model training as well as multi‑dimensional evaluation across general and domain‑specific scenarios.

AgentEvaluationLLM
0 likes · 14 min read
Defining Standard Answers for Agent‑Era LLMs: A Rubrics Survey
AI Engineer Programming
AI Engineer Programming
Jun 7, 2026 · Artificial Intelligence

Why Intent Recognition Is the Decision Hub of Agentic AI Systems

The article explains how intent recognition has evolved from simple keyword matching to a central decision hub in Agentic AI, covering basic concepts, LLM and small‑model solutions, hybrid architectures, clarification and out‑of‑scope handling, multi‑turn challenges, routing, evaluation methods, and best‑practice recommendations.

Agentic AIClarificationEvaluation
0 likes · 14 min read
Why Intent Recognition Is the Decision Hub of Agentic AI Systems
DataFunTalk
DataFunTalk
Jun 5, 2026 · Artificial Intelligence

Comprehensive Survey of Agent Harness Engineering Unveils a Seven‑Layer Framework

An extensive review of the Agent Harness Engineering survey shows that beyond model improvements, real‑world agent reliability hinges on a seven‑layer ETCLOVG framework—covering execution, tooling, context, lifecycle, observability, verification, and governance—highlighting the shift from prompt engineering to full harness engineering.

AI AgentsAgent HarnessETCLOVG
0 likes · 15 min read
Comprehensive Survey of Agent Harness Engineering Unveils a Seven‑Layer Framework
DaTaobao Tech
DaTaobao Tech
Jun 3, 2026 · Artificial Intelligence

A Comprehensive Survey of Agent Memory: Benchmarks, Evaluation Frameworks, and System Designs

This article systematically reviews the state of agent long‑term memory by covering three core dimensions—benchmark datasets such as MUSE and LOCOMO, evaluation frameworks like MemoryAgentBench, LONGMEMEVAL and MemBench, and representative memory system implementations (THEANINE, RMM, M3‑Agent, Mem0)—while highlighting key capabilities, performance gaps, and future research directions.

AgentEvaluationLLM
0 likes · 25 min read
A Comprehensive Survey of Agent Memory: Benchmarks, Evaluation Frameworks, and System Designs
Machine Heart
Machine Heart
May 31, 2026 · Artificial Intelligence

Defining a Good Answer in the Agent Era: A Rubrics Survey

This survey examines how rubrics can decompose the vague notion of a "good answer" for large language models into concrete, multi‑dimensional evaluation criteria, detailing their definition, construction methods, applications in training and evaluation, and the open challenges they present.

AI alignmentAgentic AIEvaluation
0 likes · 13 min read
Defining a Good Answer in the Agent Era: A Rubrics Survey
DataFunTalk
DataFunTalk
May 31, 2026 · Artificial Intelligence

The Most Comprehensive Survey of Agent Harness Engineering

This article summarizes the Agent Harness Engineering survey, outlining the evolution from Prompt to Context to Harness engineering, presenting the seven‑layer ETCLOVG framework, benchmark findings, and the shift toward platform‑level observability, governance, and trace‑native evaluation for reliable AI agents.

Agent HarnessETCLOVGEvaluation
0 likes · 12 min read
The Most Comprehensive Survey of Agent Harness Engineering
DataFunTalk
DataFunTalk
May 29, 2026 · Artificial Intelligence

From Prompt to Context to Harness: Unpacking the Three Paradigm Shifts in Agent Engineering

The survey "Agent Harness Engineering: A Survey" reveals how agent systems have evolved from prompt engineering to context engineering and now to harness engineering, introduces the seven‑layer ETCLOVG framework, shows benchmark gains from better harnesses, and argues that observability, governance, and trace‑native evaluation are essential for production‑grade AI agents.

AI AgentsAgent EngineeringEvaluation
0 likes · 14 min read
From Prompt to Context to Harness: Unpacking the Three Paradigm Shifts in Agent Engineering
AI Engineer Programming
AI Engineer Programming
May 29, 2026 · Artificial Intelligence

How to Build a Reliable RAG Test Dataset

The article explains why a structured test set is essential for Retrieval‑Augmented Generation systems, outlines failure modes, describes layered evaluation of retrieval and generation, details infrastructure like chunk IDs and manifests, and provides a complete annotation pipeline with cold‑start and adversarial strategies.

EvaluationLLMRAG
0 likes · 24 min read
How to Build a Reliable RAG Test Dataset
DataFunTalk
DataFunTalk
May 28, 2026 · Artificial Intelligence

The Most Comprehensive Survey on Agent Harness Engineering Revealed

This article summarizes the 71‑page survey "Agent Harness Engineering: A Survey", detailing the shift from prompt to context to harness engineering, introducing the seven‑layer ETCLOVG framework, benchmark results showing up to 10× gains, and arguing that future competition will focus on the engineering shell surrounding LLM agents rather than model size alone.

AI SystemsAgentEvaluation
0 likes · 15 min read
The Most Comprehensive Survey on Agent Harness Engineering Revealed
大转转FE
大转转FE
May 21, 2026 · Artificial Intelligence

Why AI Buzzwords Multiply Faster Than My Hair Falls

The article maps three generations of AI engineering—Prompt Engineering, Context Engineering, and Harness Engineering—explaining their core capabilities, key terms like LLM, RAG, Agent, and evaluation methods, while offering practical tips, pitfalls, and a concise three‑question checklist to stay grounded amid the rapid influx of new AI jargon.

AIAgentEvaluation
0 likes · 19 min read
Why AI Buzzwords Multiply Faster Than My Hair Falls
PaperAgent
PaperAgent
May 19, 2026 · Artificial Intelligence

Why Long-Term Memory Needs Vision: How MemEye Evaluates Multimodal Agent Recall

MemEye is a multimodal memory benchmark that tests agents across eight real‑world scenarios, measuring visual evidence granularity and reasoning depth, and reveals that captions fall short for fine‑grained visual recall, highlighting the need for true visual memory in long‑term AI agents.

AI AgentsEvaluationMemEye
0 likes · 4 min read
Why Long-Term Memory Needs Vision: How MemEye Evaluates Multimodal Agent Recall
DataFunTalk
DataFunTalk
May 19, 2026 · Industry Insights

From Single‑Point Copilot to Platform‑Level Agentic: Real Challenges and Future Forks for Data Platforms

A live discussion dissected the shift from single‑point Copilot assistants to platform‑level Agentic data platforms, exposing hard architectural, security, knowledge‑base, evaluation, stability‑cost, and governance challenges while debating whether the future will favor a super‑agent or a multi‑agent ecosystem.

Agentic AIBig DataData Platform
0 likes · 18 min read
From Single‑Point Copilot to Platform‑Level Agentic: Real Challenges and Future Forks for Data Platforms
High Availability Architecture
High Availability Architecture
May 19, 2026 · Artificial Intelligence

5 Essential Tools to Install Before Building an AI Agent

The article outlines five critical setup steps—privacy with direnv and a secret manager, token handling via litellm or portkey, context management using uv and git commits, visibility through mitmproxy, and rigorous evaluation with inspect‑ai—showing how they cut token waste by 68.3%, reduce costs 92.5% and raise evaluation pass rates to 94.2% across 347 runs.

AI AgentsEvaluationPrivacy
0 likes · 9 min read
5 Essential Tools to Install Before Building an AI Agent
DeepHub IMBA
DeepHub IMBA
May 18, 2026 · Artificial Intelligence

Self‑Improving Multi‑Agent RAG System: Architecture, Evaluation, and Human‑Reviewed Prompt Loop

An end‑to‑end multi‑agent Retrieval‑Augmented Generation platform is presented, featuring compositional reasoning, systematic multi‑dimensional evaluation, and a controlled prompt‑improvement loop that automatically identifies weak prompt dimensions, proposes diffs, and requires human approval before deployment, with full observability via SSE and persisted logs.

EvaluationFastAPIPrompt Engineering
0 likes · 19 min read
Self‑Improving Multi‑Agent RAG System: Architecture, Evaluation, and Human‑Reviewed Prompt Loop
DataFunSummit
DataFunSummit
May 18, 2026 · Artificial Intelligence

From Single‑Point Copilot to Platform‑Level Agentic: Real Challenges and Future Paths for Data Platforms

A 90‑minute live discussion examined how data platforms must evolve from simple Copilot assistants to fully agentic systems, covering architectural redesign, security guardrails, knowledge‑base integration, evaluation pitfalls, cost management, and whether the future favors a super‑agent or a multi‑agent ecosystem.

Agentic AIData PlatformEvaluation
0 likes · 20 min read
From Single‑Point Copilot to Platform‑Level Agentic: Real Challenges and Future Paths for Data Platforms
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
May 13, 2026 · Artificial Intelligence

How to Explain a Jump from 71% to 94% Tool‑Calling Accuracy in a JD Interview

The article walks through a JD interview scenario where a candidate explains how a tool‑calling accuracy metric rose from 71% to 94% by detailing the full SFT data‑engineering pipeline, teacher‑model trajectory generation, quality validation, evaluation methodology, and interview‑ready talking points.

Data EngineeringEvaluationFunction Calling
0 likes · 19 min read
How to Explain a Jump from 71% to 94% Tool‑Calling Accuracy in a JD Interview
James' Growth Diary
James' Growth Diary
May 11, 2026 · Artificial Intelligence

Mastering RAG Evaluation: Recall@K, MRR, NDCG, and RAGAS Explained

This article breaks down RAG evaluation into a two‑layer framework, explains the four core metrics—Recall@K, MRR, NDCG, and the four RAGAS scores—shows how to implement them with LangChain.js, highlights common pitfalls, and offers scenario‑specific metric combinations for reliable performance monitoring.

EvaluationLangChainMRR
0 likes · 20 min read
Mastering RAG Evaluation: Recall@K, MRR, NDCG, and RAGAS Explained
Wuming AI
Wuming AI
May 10, 2026 · Artificial Intelligence

Can Large Models Really Understand 1 M Tokens? Lessons from the RULER Benchmark

The article examines why a model’s advertised context window (e.g., 128 K or 1 M tokens) does not guarantee effective long‑context reasoning, summarizing the RULER framework that breaks long‑context ability into retrieval, interference resistance, multi‑hop tracking, aggregation, and multi‑answer recall, and offering practical guidance for evaluating and using such models.

AggregationEvaluationLLM
0 likes · 16 min read
Can Large Models Really Understand 1 M Tokens? Lessons from the RULER Benchmark
Machine Heart
Machine Heart
May 10, 2026 · Artificial Intelligence

Stop Fragmenting Long Texts: HiLight Lets AI Highlight Key Points Directly

The HiLight approach inserts lightweight highlight tags into full-length inputs, training a small Emphasis Actor to score token importance and guide a frozen large language model, improving performance on tasks like recommendation and QA without modifying the solver, while keeping low latency and training cost.

EvaluationLLMhighlighting
0 likes · 9 min read
Stop Fragmenting Long Texts: HiLight Lets AI Highlight Key Points Directly
Linyb Geek Road
Linyb Geek Road
May 5, 2026 · Artificial Intelligence

How to Fully Evaluate a RAG System – Metrics for Retrieval and Generation Stages

The article explains why RAG systems require stage‑wise evaluation, detailing retrieval metrics such as Precision, Recall, F1, MRR, NDCG and Context Relevance, and generation metrics like Faithfulness, Answer Relevance and Completeness, while discussing LLM‑as‑Judge automation and a three‑layer assessment framework.

EvaluationLLM-as-JudgeRAG
0 likes · 14 min read
How to Fully Evaluate a RAG System – Metrics for Retrieval and Generation Stages
Architect
Architect
May 4, 2026 · Artificial Intelligence

What Skills Architects Must Master in the Agent Era and Which Will Last Six Months

In the fast‑changing Agent era, architects should focus on durable engineering capabilities—context management, tool design, evaluation, harness, permissions, and cost control—rather than chasing the latest frameworks, ensuring agents remain stable and controllable in production systems.

AI AgentsContext ManagementEvaluation
0 likes · 26 min read
What Skills Architects Must Master in the Agent Era and Which Will Last Six Months
PaperAgent
PaperAgent
May 4, 2026 · Artificial Intelligence

Why Claude 4.6 Scores Only 66%: Claw‑Eval‑Live Shows Terminal Skills Aren’t Enough

The article explains that modern AI agents must be judged on actual task execution and audit evidence, and Claw‑Eval‑Live reveals that while agents can use terminals, they still fail dramatically on cross‑system workflows such as HR, management, and operations, with no model surpassing a 70% pass rate.

AI AgentsClaw-EvalEvaluation
0 likes · 7 min read
Why Claude 4.6 Scores Only 66%: Claw‑Eval‑Live Shows Terminal Skills Aren’t Enough
AI Engineering
AI Engineering
May 4, 2026 · Artificial Intelligence

Why the Big‑Model Race Is Over: Where Real Value Lies in AI Infrastructure

The article argues that the competition over which large language model will dominate is outdated, explaining that true value now comes from building multi‑model routing, context engineering, standardized tool protocols, intelligent orchestration, and robust evaluation layers that turn models into reliable AI infrastructure.

AI InfrastructureEvaluationMCP
0 likes · 6 min read
Why the Big‑Model Race Is Over: Where Real Value Lies in AI Infrastructure
PMTalk Product Manager Community
PMTalk Product Manager Community
May 4, 2026 · Product Management

2026 AI Product Manager: The Essential Capability Model

By 2026, AI product managers must shift from merely using models to delivering stable, valuable results, mastering seven core abilities—demand judgment, evaluation-driven iteration, context design, RAG strategy, agent orchestration, solution planning, and rapid Vibe Coding—to close the loop between business needs and AI capabilities.

AI product managementAgent DesignEvaluation
0 likes · 13 min read
2026 AI Product Manager: The Essential Capability Model
AgentGuide
AgentGuide
May 3, 2026 · Artificial Intelligence

How to Evaluate an AI Agent Beyond Just Accuracy

Evaluating AI agents requires more than accuracy; you must measure task completion, execution trace, tool usage, latency, cost, error rates, and both explicit and implicit user feedback, using observability, offline smoke‑test and regression suites, and continuous online monitoring to create a closed‑loop improvement process.

AI AgentEvaluationMetrics
0 likes · 14 min read
How to Evaluate an AI Agent Beyond Just Accuracy
AI Architecture Hub
AI Architecture Hub
May 3, 2026 · Artificial Intelligence

What to Learn, Build, and Skip in AI Agents

The article analyzes the fast‑changing AI‑agent landscape, proposes five concrete criteria for filtering new technologies, outlines essential concepts such as context engineering, tool design, scheduler‑subagent patterns, evaluation frameworks, and recommends a stable 2026 tech stack while warning against hype‑driven tools.

AI AgentsEvaluationLangGraph
0 likes · 27 min read
What to Learn, Build, and Skip in AI Agents
AI Engineer Programming
AI Engineer Programming
May 2, 2026 · Artificial Intelligence

From Demo to Production: How to Evaluate RAG Effectively

This guide outlines a comprehensive RAG evaluation framework covering failure modes, multi‑layer metrics, test‑set construction, open‑source tools, CI/CD quality gates, production monitoring, and special considerations for agentic RAG to ensure reliable, trustworthy retrieval‑augmented generation systems.

AIEvaluationLLM
0 likes · 18 min read
From Demo to Production: How to Evaluate RAG Effectively
MaGe Linux Operations
MaGe Linux Operations
Apr 28, 2026 · Artificial Intelligence

Why Your RAG Performance Is Poor: Common Issues and Optimization Strategies

This article systematically analyzes why Retrieval‑Augmented Generation pipelines often underperform—covering embedding model selection, chunking strategies, hybrid retrieval, reranking, context window waste, evaluation metrics, and a detailed troubleshooting checklist—while providing concrete code examples and best‑practice recommendations for engineers.

ChunkingEmbeddingEvaluation
0 likes · 19 min read
Why Your RAG Performance Is Poor: Common Issues and Optimization Strategies
PaperAgent
PaperAgent
Apr 27, 2026 · Artificial Intelligence

A Comprehensive Review of Modern LLM Agent Memory Frameworks

The article surveys recent LLM‑based agent memory research, presenting a unified framework that breaks memory systems into four components, detailing their design choices, experimental evaluation on LOCOMO and LONGMEMEVAL, key findings, and a new low‑token SOTA architecture.

Agent MemoryEvaluationInformation Retrieval
0 likes · 8 min read
A Comprehensive Review of Modern LLM Agent Memory Frameworks
AI Engineer Programming
AI Engineer Programming
Apr 23, 2026 · Artificial Intelligence

From Zero to One: A Roadmap for Building Trustworthy AI Agent Evaluations

The article outlines why rigorous, automated evaluation is essential for AI agents, defines core concepts such as tasks, trials, graders, and frameworks, compares code‑based, model‑based and human graders, and presents an eight‑step roadmap—from early testing to open‑source maintenance—to create reliable, scalable agent assessments.

AI AgentsAgent developmentBenchmarking
0 likes · 22 min read
From Zero to One: A Roadmap for Building Trustworthy AI Agent Evaluations
MaGe Linux Operations
MaGe Linux Operations
Apr 22, 2026 · Artificial Intelligence

5 Essential Design Principles for Building High‑Quality RAG Systems

This article outlines five critical design principles for constructing high‑quality Retrieval‑Augmented Generation (RAG) systems, covering document chunking strategies, embedding model selection, hybrid retrieval architectures, metadata filtering with multi‑level indexes, and reranking mechanisms, and provides concrete code snippets and evaluation metrics.

EmbeddingEvaluationHybrid Retrieval
0 likes · 17 min read
5 Essential Design Principles for Building High‑Quality RAG Systems
Su San Talks Tech
Su San Talks Tech
Apr 21, 2026 · Artificial Intelligence

How to Turn Bad Prompts into High‑Scoring AI Prompts: A Step‑by‑Step Guide

This article walks through a complete prompt‑engineering workflow—starting from a weak baseline, building an evaluation pipeline, and applying four concrete techniques (clarity, specificity, XML structuring, and examples) that lift a Claude score from 3.4 to over 9, with code, metrics, and real‑world examples.

AIClaudeEvaluation
0 likes · 19 min read
How to Turn Bad Prompts into High‑Scoring AI Prompts: A Step‑by‑Step Guide
FunTester
FunTester
Apr 20, 2026 · Artificial Intelligence

Why Self‑Evaluating Agents Fail and How to Build Reliable Multi‑Agent Systems

The article analyzes why letting the same AI Agent generate and self‑evaluate results in over‑confident but flawed outputs, especially for subjective tasks, and proposes a three‑stage multi‑agent architecture with independent evaluation, concrete standards, and prompt‑based calibration to improve reliability as models evolve.

AIEvaluationPrompt Engineering
0 likes · 9 min read
Why Self‑Evaluating Agents Fail and How to Build Reliable Multi‑Agent Systems
Java One
Java One
Apr 20, 2026 · Artificial Intelligence

From Bad Prompts to 9.5 Scores: A Step‑by‑Step Prompt Engineering Guide

This article walks through an iterative prompt‑engineering workflow—starting with a weak baseline, applying four concrete techniques (clarity & directness, specificity, XML structuring, and examples), evaluating each change with a PromptEvaluator, and showing how scores jump from 3.4 to over 9.5 using real code snippets and concrete data.

AIClaudeEvaluation
0 likes · 20 min read
From Bad Prompts to 9.5 Scores: A Step‑by‑Step Prompt Engineering Guide
Machine Heart
Machine Heart
Apr 17, 2026 · Artificial Intelligence

Can LLMs Truly Mimic Human Shopping Behavior? The OPeRA Dataset and Evaluation

The paper introduces OPeRA, a step‑wise online‑shopping dataset capturing observations, personas, rationales, and actions from real users, and uses it to benchmark LLMs on next‑action prediction, revealing that even top models like GPT‑4.1 achieve only about 20 % accuracy on fine‑grained actions, with persona information offering limited benefit while rationales prove crucial.

AIEvaluationLLM
0 likes · 9 min read
Can LLMs Truly Mimic Human Shopping Behavior? The OPeRA Dataset and Evaluation
Data Party THU
Data Party THU
Apr 16, 2026 · Artificial Intelligence

Can Multimodal LLMs Truly Understand Emotions? Inside the MME-Emotion Benchmark

The MME-Emotion benchmark, introduced by researchers from CUHK and Alibaba Tongyi and accepted at ICLR 2026, provides a large‑scale, multimodal evaluation of emotional intelligence in large language models, revealing current models’ limited emotion recognition and reasoning abilities across diverse real‑world scenarios.

AIEvaluationMME-Emotion
0 likes · 10 min read
Can Multimodal LLMs Truly Understand Emotions? Inside the MME-Emotion Benchmark
Machine Heart
Machine Heart
Apr 10, 2026 · Artificial Intelligence

Why Generalist’s Success Shifts Embodied AI Competition From Models to Infrastructure

The launch of Generalist AI’s GEN‑1 model demonstrates a breakthrough in success rate, speed and resilience, but the article argues that the true competitive frontier has moved from model performance to the underlying data, simulation and evaluation infrastructure that enables continuous learning and scalable testing for embodied intelligence.

AI modelsData InfrastructureEmbodied AI
0 likes · 12 min read
Why Generalist’s Success Shifts Embodied AI Competition From Models to Infrastructure
DataFunSummit
DataFunSummit
Apr 10, 2026 · Artificial Intelligence

How Can AI Agents Truly Remember? A Deep Dive into Long‑Term Memory Engineering

This article examines the shortcomings of current AI assistants, outlines the ideal of long‑term memory engineering, reviews mainstream industry solutions such as hard‑context models and Retrieval‑Augmented Generation, proposes a four‑layer memory loop architecture, and looks ahead to online learning and collective intelligence for future agents.

AIAgentEvaluation
0 likes · 15 min read
How Can AI Agents Truly Remember? A Deep Dive into Long‑Term Memory Engineering
Data STUDIO
Data STUDIO
Apr 10, 2026 · Artificial Intelligence

Step‑by‑Step Guide to Writing Effective Agent Skill.md Files

This article explains what Agent Skills are, shows the folder layout and SKILL.md format, introduces the progressive‑disclosure design, provides concrete best‑practice tips, testing and evaluation methods, and demonstrates how to package scripts for reliable AI‑assistant automation.

AI assistantAgent SkillsAutomation
0 likes · 29 min read
Step‑by‑Step Guide to Writing Effective Agent Skill.md Files
AI Step-by-Step
AI Step-by-Step
Apr 8, 2026 · Operations

How to Light Up the Black Box of LLM Agents with Full‑Stack Observability

The article explains why traditional logs are insufficient for LLM agents, outlines five observability dimensions—tracing, metrics, behavioral governance, state & memory, and evaluation—and provides concrete, open‑source‑based steps to instrument, monitor, and act on agent workloads in production.

Behavioral GovernanceEvaluationLLM Agents
0 likes · 11 min read
How to Light Up the Black Box of LLM Agents with Full‑Stack Observability
Machine Heart
Machine Heart
Apr 5, 2026 · Artificial Intelligence

How Imitation Learning Powers Dexterous Manipulation: A 2021‑2025 Technical Roadmap

This survey maps the 2021‑2025 progress of imitation learning for dexterous manipulation, detailing theoretical foundations, datasets, algorithms, hardware platforms, and evaluation protocols, and highlights challenges such as data quality, hardware dependence, and the need for standardized benchmarks to advance embodied AI.

Evaluationalgorithmsdatasets
0 likes · 11 min read
How Imitation Learning Powers Dexterous Manipulation: A 2021‑2025 Technical Roadmap
AI Code to Success
AI Code to Success
Apr 3, 2026 · Artificial Intelligence

Can Your AI Agent Earn a College Degree? Exploring Clawvard’s Evaluation Platform

The author explores Clawvard, an AI‑agent assessment platform that tests agents across eight dimensions, shares personal test results showing an initial A‑ rating with a critical retrieval weakness, details the customized improvement rules applied, and demonstrates a subsequent A+ rating, while also discussing the platform’s limits and practical use cases.

AI AgentEvaluationPrompt Engineering
0 likes · 8 min read
Can Your AI Agent Earn a College Degree? Exploring Clawvard’s Evaluation Platform
AgentGuide
AgentGuide
Apr 3, 2026 · Artificial Intelligence

How to Evaluate RAG Systems: Key Metrics and the Ragas Framework

The article explains how to assess Retrieval-Augmented Generation (RAG) projects using the Ragas automated evaluation framework, detailing four key dimensions—recall quality, answer faithfulness, answer relevance, and context utilization—and describes the underlying metrics for both retrieval and generation stages.

EvaluationLLMMetrics
0 likes · 5 min read
How to Evaluate RAG Systems: Key Metrics and the Ragas Framework
Top Architecture Tech Stack
Top Architecture Tech Stack
Mar 28, 2026 · Artificial Intelligence

How Anthropic’s Multi‑Agent Harness Keeps Claude Running for Hours

Anthropic’s engineering recap reveals a GAN‑inspired multi‑agent framework that separates generation, evaluation, and planning to overcome Claude’s context anxiety and self‑evaluation bias, enabling the model to sustain multi‑hour, high‑quality tasks across frontend design, full‑stack apps, and game‑engine projects.

AIClaudeEvaluation
0 likes · 19 min read
How Anthropic’s Multi‑Agent Harness Keeps Claude Running for Hours
SuanNi
SuanNi
Mar 26, 2026 · Artificial Intelligence

Unveiling Omni-WorldBench: How 18 AI Video Models Stack Up on 4D Interaction Tests

The Omni-WorldBench framework introduces a comprehensive 4D evaluation suite with 1,068 test cases and three interaction levels, applying novel metrics to assess video quality, controllability, and physical interaction fidelity across 18 state‑of‑the‑art AI video models, revealing strengths, weaknesses, and future research directions.

4D interactionEvaluationOmni-WorldBench
0 likes · 14 min read
Unveiling Omni-WorldBench: How 18 AI Video Models Stack Up on 4D Interaction Tests
SuanNi
SuanNi
Mar 25, 2026 · Artificial Intelligence

Can Harness Engineering Enable AI Agents to Master Complex Long‑Running Tasks?

This article analyses the concept of Harness engineering introduced by OpenAI and Anthropic, explains how multi‑agent architectures decompose and manage long‑running AI tasks, examines practical experiments such as a retro game maker and a web‑audio workstation, and distills lessons for future AI system design.

AI EngineeringAnthropicClaude
0 likes · 16 min read
Can Harness Engineering Enable AI Agents to Master Complex Long‑Running Tasks?
o-ai.tech
o-ai.tech
Mar 25, 2026 · Artificial Intelligence

From Code Writing to Continuous Development: Anthropic’s Long‑Running Agent Harness Design

Anthropic’s article dissects a three‑role harness—planner, generator, evaluator—for building long‑running AI applications, explaining how structured specs, sprint contracts, iterative evaluation, and context management transform a single model into a reliable software‑engineering pipeline, with concrete front‑end and full‑stack case studies.

AI AgentsEvaluationEvaluator
0 likes · 23 min read
From Code Writing to Continuous Development: Anthropic’s Long‑Running Agent Harness Design
Frontend AI Walk
Frontend AI Walk
Mar 25, 2026 · Artificial Intelligence

Slow Learning Agents: 7 Cognitive Shifts from Using ChatGPT to Truly Understanding Agents

The article outlines seven essential mindset transitions for building robust LLM agents—recognizing agents as autonomous decision loops, prioritizing harness over model size, layering context, designing tools for agent goals, structuring multi‑layer memory, coordinating multiple agents with isolation and protocols, and aligning evaluation with the real environment.

Context ManagementEvaluationHarness
0 likes · 16 min read
Slow Learning Agents: 7 Cognitive Shifts from Using ChatGPT to Truly Understanding Agents
AgentGuide
AgentGuide
Mar 22, 2026 · Artificial Intelligence

How to Design Prompt Engineering in Your Project: A Complete Workflow

The article outlines a systematic Prompt Engineering process that starts with defining task goals and metrics, structures prompts into modular components, uses offline evaluation and bad‑case analysis, incorporates RAG or tools when needed, and continuously monitors accuracy, hallucination, latency and cost.

AI workflowEvaluationFew-shot
0 likes · 7 min read
How to Design Prompt Engineering in Your Project: A Complete Workflow
ByteDance SE Lab
ByteDance SE Lab
Mar 20, 2026 · Artificial Intelligence

How to Build a Multi‑Repo Semantic Code Q&A System with OpenViking

This guide explains the challenges of multi‑repository code retrieval, presents an experimental evaluation of OpenViking's semantic search, and provides step‑by‑step instructions for installing, configuring, importing repositories, and integrating the system into AI agents and chatbots.

AI assistantEvaluationMulti-repo
0 likes · 16 min read
How to Build a Multi‑Repo Semantic Code Q&A System with OpenViking
AI Frontier Lectures
AI Frontier Lectures
Mar 16, 2026 · Artificial Intelligence

Can Multimodal LLMs Truly Understand Human Emotions? Introducing the MME-Emotion Benchmark

This article presents MME-Emotion, a large‑scale multimodal benchmark that evaluates both emotion recognition and reasoning abilities of multimodal large language models across 27 real‑world scenarios, revealing current models’ significant gaps in emotional intelligence and outlining future research directions.

AIEvaluationbenchmark
0 likes · 9 min read
Can Multimodal LLMs Truly Understand Human Emotions? Introducing the MME-Emotion Benchmark
Alibaba Cloud Developer
Alibaba Cloud Developer
Mar 16, 2026 · Artificial Intelligence

HeartBench: Building the First Chinese AI Humanization Benchmark

This article details the creation of HeartBench, a Chinese benchmark for evaluating large language models' emotional and social intelligence, describing its background, design principles, data pipeline, evaluation methods, multi‑stage versioning, blind‑test validation, and lessons for building transferable AI assessment frameworks.

AI benchmarkEmotion AIEvaluation
0 likes · 25 min read
HeartBench: Building the First Chinese AI Humanization Benchmark
PaperAgent
PaperAgent
Mar 15, 2026 · Artificial Intelligence

Why LLM Tool‑Calling Benchmarks Miss Real Users: Introducing WildToolBench

WildToolBench reveals that existing LLM tool‑calling benchmarks overlook real‑world user behavior, and a comprehensive evaluation of 58 models shows even the strongest agents achieve less than 15% session accuracy, highlighting a huge gap between reported performance and practical usability.

Agentic AIEvaluationLLM
0 likes · 10 min read
Why LLM Tool‑Calling Benchmarks Miss Real Users: Introducing WildToolBench
Old Zhang's AI Learning
Old Zhang's AI Learning
Mar 11, 2026 · Artificial Intelligence

Upgrade All Your Claude Skills Now: Harness the New Skill‑Creator Engine

Anthropic’s updated skill‑creator turns Skills into a core, engineering‑focused capability for Claude, offering a systematic workflow—baseline A/B testing, quantitative assertions, visual evaluation, and iterative description optimization—so developers can rebuild, refine, and reliably trigger their Skills for higher productivity.

AI AgentsAnthropicAutomation
0 likes · 13 min read
Upgrade All Your Claude Skills Now: Harness the New Skill‑Creator Engine
PaperAgent
PaperAgent
Mar 9, 2026 · Artificial Intelligence

How SkillNet Turns AI Agent Experience into Reusable Skills

SkillNet proposes a three‑layer infrastructure that extracts, evaluates, and connects over 200,000 AI‑agent skills into a structured graph, dramatically improving performance across benchmark environments while turning transient agent experience into durable, reusable assets.

AI AgentsEvaluationKnowledge Management
0 likes · 6 min read
How SkillNet Turns AI Agent Experience into Reusable Skills
AI Tech Publishing
AI Tech Publishing
Mar 7, 2026 · Artificial Intelligence

A Practical Guide to Evaluating Agent Skills

This article explains why many Agent Skills are released without testing, defines measurable success criteria, and presents a lightweight evaluation framework—including prompt set creation, deterministic checks, optional LLM‑based qualitative checks, and best‑practice recommendations—demonstrated by improving a Gemini Interactions API skill from 66.7% to 100% pass rate.

AI AgentsAgent SkillsEvaluation
0 likes · 13 min read
A Practical Guide to Evaluating Agent Skills
Amap Tech
Amap Tech
Mar 5, 2026 · Artificial Intelligence

How MobilityBench Measures the Real Power of AI Route‑Planning Agents

MobilityBench is an open‑source benchmark built from over 100 000 real user queries that evaluates AI route‑planning agents with a deterministic sandbox, multi‑dimensional metrics, and support for ReAct and Plan‑and‑Execute frameworks, revealing performance gaps between open‑source and closed‑source models.

AI AgentsEvaluationMobilityBench
0 likes · 6 min read
How MobilityBench Measures the Real Power of AI Route‑Planning Agents
Data Party THU
Data Party THU
Feb 18, 2026 · Artificial Intelligence

Why Top AI Agents Fail in Real Work: Inside the Trainee‑Bench Benchmark

The article analyzes the gap between high benchmark scores and poor real‑world performance of AI agents, introduces the Trainee‑Bench workplace simulator, details its three evaluation dimensions, construction steps, and reveals that even state‑of‑the‑art models achieve low success rates, highlighting the need for autonomous learning and zero‑hand‑over.

AI AgentsEvaluationTrainee-Bench
0 likes · 11 min read
Why Top AI Agents Fail in Real Work: Inside the Trainee‑Bench Benchmark
AI Engineering
AI Engineering
Jan 29, 2026 · Artificial Intelligence

How a Tiny AGENTS.md Change Boosted AI Coding Accuracy from 53% to 100%

A Vercel team experiment shows that replacing the Skills approach with a small 8 KB AGENTS.md file raised AI coding agents' pass rate from 53% to a perfect 100%, revealing the fragility of explicit tool calls and the strength of passive, always‑available context.

AGENTS.mdAI coding agentsEvaluation
0 likes · 11 min read
How a Tiny AGENTS.md Change Boosted AI Coding Accuracy from 53% to 100%
JD Tech
JD Tech
Jan 27, 2026 · Artificial Intelligence

How Uni-Layout Unifies Cross‑Task Layout Generation with Human‑Like Evaluation

Uni-Layout introduces a unified framework that integrates a universal layout generator, a human‑feedback‑simulating evaluator, and a dynamic margin preference optimization technique to align generation and evaluation across diverse e‑commerce design tasks, backed by a new 100k human‑annotated dataset.

EvaluationHuman Feedbackdynamic margin optimization
0 likes · 11 min read
How Uni-Layout Unifies Cross‑Task Layout Generation with Human‑Like Evaluation
Smart Era Software Development
Smart Era Software Development
Jan 27, 2026 · Artificial Intelligence

Why Evaluation and Governance Are the Key to Scaling AI Agents

As 82% of organizations plan to adopt AI agents within three years, this article outlines a full‑chain methodology—7‑dimensional classification, multi‑layer evaluation metrics, three‑stage validation, five‑step risk lifecycle, and progressive governance—to safely scale autonomous agents from prototype to enterprise deployment while addressing emerging multi‑agent challenges.

AI AgentsEvaluationGovernance
0 likes · 22 min read
Why Evaluation and Governance Are the Key to Scaling AI Agents
Architect
Architect
Jan 19, 2026 · Artificial Intelligence

How Cursor Scales Autonomous Coding Agents to Hundreds: Architecture Lessons for AI Systems

This article analyzes Cursor's engineering choices for running autonomous coding agents at scale, detailing the long‑running, drift, and evaluation concepts, the Planner‑Worker‑Judge pipeline, concurrency challenges, experimental results, and actionable rules for building robust multi‑agent systems.

Evaluationsoftware engineeringsystem architecture
0 likes · 17 min read
How Cursor Scales Autonomous Coding Agents to Hundreds: Architecture Lessons for AI Systems
Old Zhao – Management Systems Only
Old Zhao – Management Systems Only
Jan 15, 2026 · Operations

Why Most Supplier Evaluation Systems Fail and the 4 Metrics That Actually Matter

The article explains why traditional supplier evaluation forms often become meaningless, introduces four decisive metrics—delivery stability, quality consistency, cost transparency, and collaboration willingness—provides concrete scoring formulas for each, and shows how an SRM system can automate and visualize these indicators to help companies decide whether to replace a supplier.

EvaluationOperationsSRM
0 likes · 10 min read
Why Most Supplier Evaluation Systems Fail and the 4 Metrics That Actually Matter
JD Cloud Developers
JD Cloud Developers
Jan 15, 2026 · Artificial Intelligence

Uni-Layout: Unifying Layout Generation with Human Feedback and Dynamic Alignment

Uni-Layout introduces a unified framework that combines a multimodal large language model‑based generator, a human‑like evaluator trained on the large Layout‑HF100k dataset, and a Dynamic Margin Preference Optimization (DMPO) method to align generation and evaluation, achieving state‑of‑the‑art results across diverse layout tasks.

DMPOEvaluationHuman Feedback
0 likes · 11 min read
Uni-Layout: Unifying Layout Generation with Human Feedback and Dynamic Alignment
JD Tech Talk
JD Tech Talk
Jan 15, 2026 · Artificial Intelligence

Uni-Layout: Harnessing Human Feedback for Unified Layout Generation and Evaluation

Uni-Layout introduces a unified framework that generates layouts across diverse tasks, simulates human evaluation with a novel feedback dataset, and aligns generation and assessment through dynamic margin preference optimization, achieving state‑of‑the‑art performance on multiple benchmarks.

AI designEvaluationHuman Feedback
0 likes · 11 min read
Uni-Layout: Harnessing Human Feedback for Unified Layout Generation and Evaluation
AI Insight Log
AI Insight Log
Jan 10, 2026 · Artificial Intelligence

Anthropic’s Full Practical Guide to Evaluating AI Agents – Key Insights

The article explains why evaluating AI agents is far more complex than testing deterministic code, outlines Anthropic’s anatomy of a complete evaluation system—including tasks, transcripts, and three grader types—and offers concrete best‑practice recommendations for building reliable agent pipelines.

AI AgentsAnthropicEvaluation
0 likes · 9 min read
Anthropic’s Full Practical Guide to Evaluating AI Agents – Key Insights
JD Retail Technology
JD Retail Technology
Jan 8, 2026 · Artificial Intelligence

Uni-Layout: Unified Cross-Task Layout Generation with Human-Aligned Evaluation

Uni-Layout introduces a unified layout generation framework that consolidates diverse design tasks, leverages multimodal large language models for flexible generation, and aligns outputs with human perception through a novel human‑feedback dataset (Layout‑HF100k) and a dynamic margin preference optimization (DMPO) evaluator.

ACM MultimediaEvaluationHuman Feedback
0 likes · 11 min read
Uni-Layout: Unified Cross-Task Layout Generation with Human-Aligned Evaluation
DataFunSummit
DataFunSummit
Jan 3, 2026 · Artificial Intelligence

What Is Memory Engineering? Unlocking AI’s Long‑Term Recall and Future Potential

A comprehensive dialogue among industry experts explores the concept of memory engineering for AI agents, covering its definition, system‑level challenges from edge to cloud, hybrid technical routes, evaluation metrics, privacy safeguards, audience questions, future directions, and practical advice for developers.

AI AgentsEvaluationHybrid Architecture
0 likes · 24 min read
What Is Memory Engineering? Unlocking AI’s Long‑Term Recall and Future Potential
AI Product Manager Community
AI Product Manager Community
Dec 27, 2025 · Product Management

Embracing Uncertainty: Redesigning AI Product Requirements

The article explores how product managers must shift from deterministic PRDs to uncertainty‑driven specifications for AI chatbots, replacing exhaustive logic with value‑based constraints, fuzzy‑evaluation metrics, dynamic benchmarks, and sample‑based requirements to better align with probabilistic large‑model behavior.

AIEvaluationPRD
0 likes · 9 min read
Embracing Uncertainty: Redesigning AI Product Requirements
Alibaba Cloud Native
Alibaba Cloud Native
Dec 19, 2025 · Artificial Intelligence

What Enterprises Are Learning from the State of Agent Engineering Report

The recent LangChain "State of Agent Engineering" report, combined with data from the AI‑Native Application Architecture whitepaper, reveals rapid production adoption of AI agents, persistent quality challenges, widespread observability, multi‑model strategies, and evolving evaluation practices across organizations of all sizes.

AI AgentsEvaluationLLM
0 likes · 10 min read
What Enterprises Are Learning from the State of Agent Engineering Report
Model Perspective
Model Perspective
Dec 19, 2025 · Fundamentals

How a Multi‑Dimensional Model Ranks China’s Historical TV Dramas

This study builds a comprehensive evaluation model for Chinese historical drama series, defining four primary and nine secondary indicators, standardizing data, applying weighted calculations and a time‑compensation factor to score 127 candidates and produce a TOP‑100 ranking that highlights the influence of audience reputation, market impact, professional recognition, and historical value.

EvaluationModelRanking
0 likes · 18 min read
How a Multi‑Dimensional Model Ranks China’s Historical TV Dramas
Youzan Coder
Youzan Coder
Nov 21, 2025 · Artificial Intelligence

How to Build, Evaluate, and Optimize AI Test Agents: A Practical Guide

This guide walks you through creating AI‑powered test agents, defining success metrics, building evaluation datasets, crafting and refining system prompts with techniques like chain‑of‑thought, XML, few‑shot and concise inputs, and scaling the workflow by splitting agents and managing prompt versions.

AI AgentsEvaluationLLM
0 likes · 21 min read
How to Build, Evaluate, and Optimize AI Test Agents: A Practical Guide
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Nov 4, 2025 · Artificial Intelligence

Why Financial RAG Fails and How to Solve Its Core Challenges

This article explains why Retrieval‑Augmented Generation (RAG) projects in the financial sector often underperform, highlighting data‑structure complexities, document‑parsing hurdles, chunking strategies, compliance constraints, evaluation metrics, and engineering requirements, and offers practical solutions and code examples.

ChunkingEvaluationRAG
0 likes · 10 min read
Why Financial RAG Fails and How to Solve Its Core Challenges