Evals

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

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,

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

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

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

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