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Advanced Prompt Engineering Guide
64
Lecciones
8
Módulos
🎓
Acceso por bootcamp
Lo que aprenderás
✓Design prompts systematically using structured frameworks, not intuition
✓Master zero-shot and few-shot prompting with example engineering and boundary testing
✓Obtain reliable structured outputs (JSON, Pydantic schemas) across multiple providers
✓Implement Chain-of-Thought reasoning with verification patterns for math, logic, and code
✓Apply ReAct (Reasoning + Acting), Self-Consistency, and Tree-of-Thought for complex tasks
✓Compose multi-stage prompt pipelines with chaining, decomposition, and context management
✓Evaluate prompts with quantitative metrics: LLM-as-judge, regression testing, A/B testing
✓Manage prompts in production: versioning, caching, cost optimization, monitoring, and deployment
¿Para quién es?
- •AI Engineers who use LLMs in production and need consistent, evaluable prompts
- •Python developers building applications with OpenAI/Anthropic who want to go beyond ad-hoc prompting
- •Backend engineers integrating LLMs into existing systems that require structured, reliable outputs
- •Engineers who completed LangChain & LangGraph and want to master the prompting layer
- •Anyone preparing for AI Engineering roles where prompt design is a core competency
Requisitos
- •Intermediate Python (functions, classes, basic async/await)
- •Experience calling LLM APIs (OpenAI, Anthropic, or similar)
- •Familiarity with REST APIs and JSON
- •At least one LLM API key (OpenAI recommended, Anthropic optional)
- •Python 3.11+ installed
Contenido del curso
1Módulo 1: Fundamentos de Prompt Engineering8 lecciones
2Módulo 2: Zero-Shot y Few-Shot Prompting8 lecciones
3Módulo 3: Structured Outputs y System Prompts8 lecciones
4Módulo 4: Chain-of-Thought y Razonamiento8 lecciones
5Módulo 5: ReAct, Self-Consistency y Patrones Avanzados8 lecciones
6Módulo 6: Prompt Composition y Chaining8 lecciones
7Módulo 7: Evaluación de Prompts8 lecciones
8Módulo 8: Prompt Engineering en Producción8 lecciones
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