A clear way to when AI models can rely on synthetic data and when human feedback remains essential for alignment, safety, and frontier performance.

A clear comparison between fine-tuning and RLHF to help ML and product teams choose the right LLM training strategy based on goals, cost, and data needs.

Discover essential fine-tuning methods for large language models to customize AI performance for specific tasks and industries.

See how real user input shapes better AI-improving trust, relevance, and business results. Get insights on building smarter, people-focused models.

Reinforcement learning from human feedback (RLHF) trains AI models to align with human values through supervised fine‑tuning, reward modeling, and policy optimization.

Reinforcement Learning from Human Feedback (RLHF) improves AI by using human input to fine‑tune models, making outputs safer, accurate, and aligned with user needs.