Evals

Challenges in Managing High-Quality Datasets for LLM Evaluation

Challenges in Managing High-Quality Datasets for LLM Evaluation

TL;DR Managing high-quality datasets for LLM evaluation presents significant challenges that directly impact model performance and reliability. Research shows that models trained with poor data quality can experience a precision drop from 89% to 72%, demonstrating the critical importance of data curation. Organizations face hurdles including dataset scalability issues,

Iterative Development of AI Agents: Tools and Techniques for Rapid Prototyping and Testing

Iterative Development of AI Agents: Tools and Techniques for Rapid Prototyping and Testing

TL;DR Building reliable AI agents requires disciplined iteration through simulation, evaluation, and observability. This guide outlines a practical workflow: simulate multi-turn scenarios with personas and realistic environments, evaluate both session-level outcomes and node-level operations, instrument distributed tracing for debugging, and curate production cases into test datasets. By closing the

5 Strategies for A/B Testing for AI Agent Deployment

5 Strategies for A/B Testing for AI Agent Deployment

TL;DR A/B testing for AI agents compares controlled variants of prompts, workflows, and models against defined success metrics. Combine offline simulation, targeted evals, and in‑production observability to detect regressions, quantify impact, and iterate safely. Use an AI gateway for consistent routing, cost and latency telemetry, and repeatable

Incorporating Human-in-the-Loop Feedback for Continuous Improvement of AI Agents

Incorporating Human-in-the-Loop Feedback for Continuous Improvement of AI Agents

The deployment of AI agents to production creates a fundamental challenge: how do you ensure your agents continue improving based on real-world performance rather than static test sets? While automated evaluation provides scalability, human judgment remains essential for capturing nuanced quality dimensions, validating edge cases, and aligning AI behavior with

Auto Evaluation in AI Development: How to Automate the Assessment of Agent Performance

Auto Evaluation in AI Development: How to Automate the Assessment of Agent Performance

Deploying AI agents to production presents a critical challenge: ensuring consistent quality at scale. As AI systems handle thousands of interactions daily, manual quality assessment becomes impractical and introduces bottlenecks that slow down iteration cycles. Auto evaluation (the automated assessment of AI agent performance using predefined metrics and criteria) has

Measuring LLM Hallucinations: The Metrics That Actually Matter for Reliable AI Apps

Measuring LLM Hallucinations: The Metrics That Actually Matter for Reliable AI Apps

LLM hallucinations aren’t random; they’re measurable. This guide breaks down six core metrics and explains how to wire them into tracing and rubric-driven evaluation so teams can diagnose failures fast and ship reliable AI agents with confidence.

Choosing an Evaluation Platform: 10 Questions to Ask Before You Buy

Choosing an Evaluation Platform: 10 Questions to Ask Before You Buy

Introduction: Why Choosing the Right Evaluation Platform Matters An evaluation platform helps measure, test, and monitor AI workflows across different stages; experimentation, pre-release testing, and production; depending on what the platform actually supports. For teams building AI agents, chatbots, or RAG pipelines, the right platform enables faster iteration, early quality