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IntermediatecourseBootcamp access

Advanced Prompt Engineering Guide

64

Lessons

8

Modules

🎓

Bootcamp access

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

Course content

1Módulo 1: Fundamentos de Prompt Engineering8 lessons
2Módulo 2: Zero-Shot y Few-Shot Prompting8 lessons
3Módulo 3: Structured Outputs y System Prompts8 lessons
4Módulo 4: Chain-of-Thought y Razonamiento8 lessons
5Módulo 5: ReAct, Self-Consistency y Patrones Avanzados8 lessons
6Módulo 6: Prompt Composition y Chaining8 lessons
7Módulo 7: Evaluación de Prompts8 lessons
8Módulo 8: Prompt Engineering en Producción8 lessons
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