LLM Evaluation

Discover why traditional binary pass/fail grading fails for LLM applications and learn how to implement a severity-based evaluation framework to prevent catastrophic production failures.

Learn how to move from fragile prompt chains to structured AI agent workflows using Pydantic, validation gates, and per-step evaluation to achieve a 94% task success rate.

Move beyond subjective 'vibe checks' to automated, metric-driven evaluation for LLM applications. Learn how to build a robust judge ensemble, manage golden datasets, and integrate evaluation into your CI/CD pipeline.

Learn how to transition from subjective 'vibe checks' to automated, metric-driven LLM evaluation pipelines that catch hallucinations and ensure production stability.

A deep dive into the disconnect between AI agent autonomy and evaluation trust, revealing why 50% of enterprise agents fail in production despite passing internal tests.

A deep dive into the data architectures required to build reliable AI agents, covering trajectories, tool-calling datasets, and evaluation frameworks for high-performance LLMs.

Large Language Model benchmarks like MMLU and GSM8K often mask the tail-end failures that cause production outages. Learn why the mean is a dangerous metric and how to build a reliability-first evaluation framework.

OpenAI introduces Deployment Simulation, a revolutionary method to predict AI model performance and safety using real-world conversation data before public release.

Discover how Braintrust leverages OpenAI Codex and GPT-5.5 through advanced experimentation to transform customer requests into production-grade code, significantly reducing development cycles.

A deep dive into the essential terminology of AI Agents, exploring the critical differences between evaluation harnesses, execution scaffolds, and the future of agentic workflows.

Move beyond the 'vibe check' by implementing a Python-based evaluation framework that separates attribution, specificity, and relevance to ensure production-ready LLM outputs.

Move beyond qualitative 'vibe checks' to quantitative, data-driven evaluation frameworks for LLMs and AI agents using scorecards.