Prompt Engineering: From Zero-Shot to Retrieval Augmented Generation
A mesma pergunta feita a um modelo de linguagem pode gerar uma resposta genérica, uma resposta certeira ou uma resposta completamente errada — e a diferença, na maioria das vezes, não está no modelo, está em como o prompt foi construído. Engenharia de Prompts é a disciplina de projetar a entrada de um LLM (instruções, exemplos, contexto, formato esperado) para extrair o resultado mais útil e…
The article discusses the discipline of Prompt Engineering, which involves designing input prompts for Large Language Models (LLMs) to extract the most useful and reliable results possible. It highlights six techniques used in Prompt Engineering, including Zero-shot, Few-shot, Chain-of-Thought, Self-consistency, RAG, and ART, each resolving specific limitations and complexities in tasks such as classification, mathematics, and logic.
These techniques enable LLMs to improve their performance in tasks like sentiment classification, problem-solving, and providing accurate responses. The article provides examples and explanations of each technique, showcasing their applications and benefits.
Written by urgent.news from Dev.to's report — not a translation of it. Machine-written — may contain errors; check the original before relying on it.
