OpenAI

[Evals Workshop] Mastering AI Evaluation: From Playground to Production

Jul 1, 2025 · 1:25:08

In this workshop, Braintrust Solutions Engineers Carlos Esteban and Doug guide participants through the complete AI evaluation lifecycle, from offline testing in the playground to production monitoring. They explain the three core ingredients of an eval—task, data set, and score—and demonstrate how to run evals both via the Braintrust UI and the SDK. The session covers LLM-as-judge vs. deterministic code scores, the importance of starting small with synthetic data, and how to use online scoring and logging to capture real user feedback. Human-in-the-loop review is highlighted as a way to establish ground truth and close the feedback loop. The presenters also address audience questions on bootstrapping data sets, non-determinism in LLM judges, and integrating evals into existing projects.

RAG in 2025: State of the Art and the Road Forward — Tengyu Ma, MongoDB (acq. Voyage AI)

Jun 27, 2025 · 18:48

Tengyu Ma, Chief AI Scientist at MongoDB (acquired Voyage AI), argues that retrieval-augmented generation (RAG) will outlast fine-tuning and long-context models for enterprise AI because it mirrors how humans use libraries—retrieving only relevant information rather than memorizing or reprocessing entire corpora. He reports that Voyage's embedding models now achieve ~80% average accuracy across 100 datasets, with half exceeding 90%, and that 100x storage compression via matryoshka learning and quantization loses only 5–10% accuracy. Ma advocates hybrid search with rerankers and domain-specific embeddings (e.g., for code) to further improve retrieval. He predicts the model layer will absorb current 'tricks' like query decomposition and contextual chunking, simplifying RAG pipelines. New products like multimodal embeddings (accepting screenshots of PDFs, tables, videos) and auto-chunking embeddings that incorporate cross-chunk metadata are designed to shift complexity from users to model providers.