Human-in-the-loop (HITL) is an oversight pattern where humans review, approve, or intervene in AI decisions. Learn how HITL works, when it is required, and its tradeoffs.

Fine-tuning adapts a pre-trained language model for specific tasks using methods like SFT, LoRA, and RLHF. Learn when to fine-tune, costs, risks, and the 2026 recommended workflow.

Retrieval-Augmented Generation (RAG) is an architecture pattern that enhances a large language model’s responses by first retrieving relevant information from external knowledge sources—such as documents, databases, or APIs—and then feeding that retrieved context into the model alongside the user’s query. Rather than relying solely on what the model memorized during pre-training, RAG lets it look…

The EU AI Act (Regulation 2024/1689) is the world’s first comprehensive AI law. Learn about its risk-based framework, enforcement timeline, penalties up to €35M, and what it means for companies worldwide.

An AI hallucination occurs when a generative AI model produces outputs that sound plausible and authoritative but are factually incorrect, irrelevant, or entirely fabricated—presenting them with the same confidence as accurate information.