Foundation models including LLMs and multi-modal models released by OpenAI (GPT), Anthropic (Claude), Google (Gemini), Meta (Llama), and others have shown impressive capabilities across a range of tasks. Some key drivers of this performance — such as investments in GPUs/compute, model size, and pre-training data — are relatively well understood.

This talk will focus on a less understood, yet extremely powerful lever that creates significant differentiation and competitive advantage among state-of-the-art models: the use of expert human data for Evaluations (“Evals”), Supervised Fine Tuning (“SFT”), Reinforcement Learning with Human Feedback (“RLHF”), and Direct Preference Optimization (“DPO”).

The talk will also outline some best practices for maximizing returns on financial investments in human data to achieve optimal model performance. This includes effective strategies for sourcing, vetting, hiring, and managing expert human data teams, as well as task design for Evals, SFT, RLHF, and DPO, along with processes and tooling to optimize team performance, data quality and throughput.