Evals

Building a Robust Evaluation Framework for LLMs and AI Agents

Building a Robust Evaluation Framework for LLMs and AI Agents

TL;DR Production-ready LLM applications require comprehensive evaluation frameworks combining automated assessments, human feedback, and continuous monitoring. Key components include clear evaluation objectives, appropriate metrics across performance and safety dimensions, multi-stage testing pipelines, and robust data management. This structured approach enables teams to identify issues early, optimize agent behavior systematically,

Utilizing Human-in-the-Loop (HITL) Feedback for Robust AI Evaluation

Utilizing Human-in-the-Loop (HITL) Feedback for Robust AI Evaluation

TL;DR Human-in-the-loop evaluation fills critical gaps that automated evaluators miss in agentic AI systems. This guide explains how to integrate Human-in-the-loop with machine evaluators, distributed tracing, and production observability. You'll learn when to route interactions to humans, how to structure effective rubrics, and how to convert feedback

A/B Testing Strategies for AI Agents: How to Optimize Performance and Quality

A/B Testing Strategies for AI Agents: How to Optimize Performance and Quality

A/B testing has evolved from a simple website optimization technique to a critical methodology for evaluating and improving AI agent performance. As enterprises deploy increasingly sophisticated agentic AI systems, traditional testing approaches often fall short. AI agents are transforming A/B testing from a blunt instrument into a precision

Integrating Human Feedback to Enhance AI Evaluation

Integrating Human Feedback to Enhance AI Evaluation

Human feedback has become essential for building production-ready AI systems. While automated evals provide speed and consistency, they cannot capture the nuanced quality requirements that define real-world AI performance. OpenAI's research on InstructGPT demonstrated that a 1.3B parameter model trained with human feedback outperformed a 175B parameter

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