How experts shape large language models using real-world knowledge, human feedback, and domain-specific tasks.
What you will learn
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Understand how large language models (LLMs) are trained, including the role of tokens, next-token prediction, and context windows.
Identify the different types of human data used to improve AI systems, from annotation to evaluation tasks.
Explain the role of domain experts in shaping AI models through reinforcement learning from human feedback (RLHF).
Describe how micro1 connects certified professionals with real-world opportunities in the human data and AI training ecosystem.
Recognize the value of expert judgment in tasks where AI struggles, such as reasoning, ambiguity, or real-world decision-making.
Learn how human feedback influences AI behavior and why quality, diversity, and clarity in data matter.
Gain clarity on the certification process at micro1 and how to contribute to building safer, more capable AI systems.
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