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How to collect training data for AI models
AI training data should match the model’s task, users, quality requirements, and permitted use. Discover how to collect training data, compare internal, public, commercial, and synthetic sources, evaluate quality and rights, and build a repeatable process for preparing datasets for model training and evaluation.
AI agent monitoring & observability guide
AI agent monitoring helps teams track whether deployed agents complete acceptable work, use tools correctly, stay within defined limits, and perform efficiently over time. Effective monitoring combines task-level metrics, workflow traces, observability signals, and ongoing evaluation to help teams investigate failures and improve production performance.
AI performance metrics & KPIs: Enterprise guide
AI performance metrics and KPIs help teams measure model quality, agent effectiveness, operational efficiency, reliability, safety, and business impact. Explore how to choose the right measures, set meaningful targets, build representative evaluations, and track performance over time using a practical scorecard.
AI observability: A complete guide
AI observability helps teams understand what happened across an AI workflow, investigate failures, and connect technical performance with output quality. Learn how observability works, which signals and tools it uses, the challenges teams face in production, and how evaluation can strengthen monitoring and decision-making.
Latest Expert resources
View Expert resourcesNew jobs emerging due to AI in 2026
From AI trainers and evaluators to governance and leadership positions, companies across industries like healthcare, finance, consulting, and technology are looking for professionals to support AI systems and workflows in 2026.
14 best entry-level AI jobs: A beginner’s guide
Explore some of the best entry-level AI jobs for beginners in 2026, including remote and freelance opportunities in AI training, prompt engineering, etc. Learn what skills employers look for, average pay ranges from job sites, and how to get started in the growing AI job market.
DPO vs. RLHF: Comparison and when to use each
What are the differences between DPO vs. RLHF, including how each alignment method works, their costs, performance trade-offs, and when to use each one for LLM training?
What is DPO in AI? Everything you need to know
Learn about direct preference optimization (DPO), how it works, how it compares with RLHF, and when to use it for efficient LLM alignment and fine-tuning.
SFT vs. RLHF: Compare LLM training approaches
Compare SFT vs. RLHF to understand how each LLM training method works, when to use supervised fine-tuning or preference optimization, and how newer approaches like DPO, GRPO, and RFT are reshaping AI alignment.
What is RLAIF in AI alignment, and how does it work?
Learn what RLAIF (Reinforcement Learning from AI Feedback) is, how it works, how it compares with RLHF, its benefits and limitations, and when AI feedback makes sense in LLM alignment.
Latest APEX resources
View APEX resourcesLLM evaluation metrics: A complete guide
LLM evaluation metrics help teams measure different aspects of model and application performance, from correctness and groundedness to task completion, latency, and cost. Learn how these metrics are scored, interpreted, and combined so evaluations reflect real-world requirements instead of relying on a single overall score.
LLM evaluation frameworks: A complete guide
LLM evaluation frameworks help teams test model and application performance with consistent data, metrics, scoring methods, and review processes. Compare popular frameworks, understand the metrics and tradeoffs involved, and learn how to choose an approach that supports reliable testing and real-world decision-making.
What is an AI agent & how does it work?
AI agents can move beyond generating responses to taking actions toward a goal. Learn how AI agents work, what their core components are, where businesses can use them, the risks they introduce, and how teams can evaluate agent performance with realistic workflows and benchmarks.
LLM evaluation guide: Methods & best practices
Learn how LLM evaluation works, why it matters, and which methods, metrics, workflows, and benchmarks teams can use to measure model and application performance. This guide also covers common limitations, best practices, and tools for building more reliable evaluations.
Best AI models for corporate lawyer agents
Discover the best AI models for corporate lawyers based on the APEX-Agents benchmark. Compare leading AI agents, evaluate performance on real legal tasks, and see what the results mean for law firms adopting AI.
Best AI models for coding & software engineering work
Discover the best AI models for coding and software engineering tasks as of June 2026 based on Mercor's APEX-SWE benchmark. Compare top-performing models, learn which excel at code generation, debugging, and integration work, and see how llm benchmarks translate to real-world engineering performance.
