ChatGPT and peers rely on instruction tuning, RLHF, and CoT for dialog agents

AI Impact Summary

The article consolidates how modern dialog agents achieve usefulness through instruction following powered by IFT, SFT, and RLHF, with CoT prompting boosting reasoning and safety. It notes that a tiny fraction of high-quality data can yield strong instruction-fine-tuning results, while bootstrapping and diverse templates expand coverage. A practical takeaway for engineers is to implement modular pipelines for data collection, human feedback, safety rules, and evaluation focused on alignment and groundedness across models. This multi-model perspective implies plan for vendor-agnostic benchmarking and migration options when switching base models.