RLHF vs. Fine-Tuning: What's the Difference, and Which Do You Need?
A direct head-to-head between supervised fine-tuning (SFT) and RLHF: what each one mechanically does, the different kind of data each needs and what that data costs to produce, a side-by-side comparison table, a worked support-ticket example, where DPO fits as RLHF's simplified alternative, and a practical decision framework for which to use when.
Read the guideRLHF & Preference Data Explained: The Human Data Behind Aligned AI
The definitive explainer on RLHF and preference data: what reinforcement learning from human feedback is, how preference pairs and ranked lists are structured, why this data is so scarce and valuable, how it is collected and labeled, the quality signals that separate great data from bad, and how to source or sell it. Cited and data-backed.
Read the guideWhat Is Human-in-the-Loop AI Training?
Human-in-the-loop (HITL) is the general paradigm of humans participating in an ML system's training or operation, broader than labeling or RLHF alone. This guide covers the six points where humans loop in, how active learning cuts labeling cost, the cost/latency/scalability tradeoffs, and the industry's shift toward AI-assisted human review.
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