Prompt

GLOSARIO

Few-Shot Learning: Definition, Mechanism, and Limits of In-Context Learning

Few-shot prompting is a context engineering technique that includes input-output pairs in the request to guide the model's response without updating its weights. This entry explains the in-context learning mechanism, provides three applied examples in different domains, distinguishes the technique from zero-shot, fine-tuning, and chain of thought, and outlines its operational constraints.

21 Oct 2026 ↗

ANALISIS

From Answering to Acting: How the Interface Between People and AI Has Changed

The evolution of interaction with AI can be understood as a change in what people ask a system to do and what the system can do in response. But the available sources document examples of interfaces and assistance—not a uniform historical transition or a general ability to carry out actions.

30 Sep 2026 ↗

GLOSARIO

Context Engineering: How to Select and Maintain the Information an AI System Receives

Context engineering means deciding what information a model needs for a task, how to obtain it, and how to keep it relevant while the task is underway. It is not the same as writing a longer prompt: it also involves selecting documents, using tools, and managing state.

27 Sep 2026 ↗

GLOSARIO

Chain of Thought in AI: What It Is—and What It Doesn’t Prove

A chain of thought is a sequence of intermediate steps expressed in text that a model generates or receives as an example. It can help structure a task and make some steps visible, but coherence does not prove that those steps are correct or faithfully reflect the internal process that produced the answer.

27 Sep 2026 ↗