Fundamentos de los LLMs
El poder del contexto en el Prompt
Vectores, Embeddings y Espacios N-Dimensionales
Tokenización
El Mecanismo de Atención y Razonamiento en Modelos de IA
El Playground de OpenAI
Tipos de Prompts y sus Aplicaciones
Zero-Shot Prompting y Self-Consistency
Técnicas para refinar un prompt Zero Shot
Few-Shot Prompting
Chain of Thought y Prompt Chaining
Meta-Prompting
Técnicas Avanzadas de Prompt Engineering
Iteración de Prompts
Least to most prompting
Prompt Chaining
Uso de Restricciones y Formatos de Respuesta
Optimización y Aplicaciones del Prompt Engineering
Generación de Imágenes con GPT4o y Generación de Audio
Ajustando la Temperatura y el Top P
Imagine a gigantic library containing absolutely everything ever written by mankind. Now imagine this library with a mind of its own, capable of meaningfully understanding the contents. This is precisely what an LLM or large language model is, a tool that although it has no consciousness, possesses the impressive ability to predict the next word or phrase with great accuracy.
A large language model, when properly instructed, can perform tasks similar to those of a young professional fresh out of school:
The key to getting the most out of an LLM is to give clear and contextual instructions. It is not about generating extremely long texts, but rather writing effectively and accurately. Every word that is included really matters.
If you want to get the most out of this technology, it is essential:
The effectiveness of a well-structured prompt goes beyond answering a question; it becomes a true working guide that can surpass any internal documentation.
Learning to create efficient prompts not only streamlines internal processes, but also frees up your time, allowing you to fully dedicate yourself to bigger challenges and real opportunities for professional growth.
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