GPT-5 Mini

Shipping complex AI applications — Braintrust & Trainline

May 1, 2026 · 1:38:34

Giran Moodley of Braintrust, joined by Trainline's Oussama Hafferssas and Mayank Soni, demonstrate how to ship production-grade multi-step AI agents by combining rigorous tracing, evaluation with golden datasets, and automated online scoring. They walk through building a support triage agent that progresses from a single prompt to a five-stage tool-calling pipeline, where tracing captures latency, tokens, and costs per step for deep debugging. Trainline shares using Braintrust to run offline evaluations before switching LLM models and to enable cross-functional self-service. The workshop covers identifying failure modes via production logs, tightening prompts, and re-evaluating to complete the feedback loop, emphasizing that observability and iterative evaluation are essential for moving from prototype to reliable production systems.

Let LLMs Wander: Engineering RL Environments — Stefano Fiorucci

Apr 8, 2026 · 40:35

Stefano Fiorucci demonstrates how to build Reinforcement Learning environments for language models using the open-source Verifiers library, arguing that training small models with verifiable rewards can surpass large closed models on specific tasks. He maps classic RL concepts to LLMs, introduces Verifiers components for single-turn, multi-turn, and tool environments, and then walks through an experiment where he takes LiquidAI's LLM 2 — a small open model — and transforms it into a tic-tac-toe master via supervised fine-tuning and GRPO-based reinforcement learning. After training, the model dominates random opponents and draws 85% of games against optimal ones, eventually outperforming GPT-5 Mini against identical optimal opponents. Fiorucci shares practical lessons: large batch sizes (≥256) ensure stable training, hidden biases in opponent algorithms can skew results, and starting from a base model (not a reasoning model) avoids truncated thinking traces. He concludes that if you can define a clear reward signal, you can build an environment and train a small specialized model to beat a large closed model at a fraction of the cost.