Enjoy a 25% discount on our courses this winter 2025.Enjoy a 25% discount on our courses this winter 2025.
Join this course designed for those interested in understanding and mastering the technique of creating effective prompts—essential for directing interactions between humans and language models. Be part of the Fourth Industrial Revolution, boost your skills, and optimize your daily tasks efficiently.
In 2025, effective interaction with Artificial Intelligence is an indispensable skill. This course immerses you in a fully practical, hands-on introduction to Prompt Engineering, teaching you to communicate with the most advanced Large Language Models (LLMs) without writing a single line of code. Through direct use of web interfaces and LLM playgrounds (such as those offered by OpenAI, Google AI, Anthropic, and others), you’ll learn fundamental principles and advanced techniques to get the best possible outcomes from AI. You’ll explore how to structure prompts to generate text, summarize, translate, extract information, generate creative ideas, and much more. The course covers powerful techniques like Few-Shot Learning (learning from examples), Chain-of-Thought (guiding step-by-step reasoning), and how to effectively use external information provided in the prompt (the RAG prompting segment). We will also address critical considerations for 2025, including ethics, bias detection, safety against prompt injection, and how to evaluate response quality. If you use AI tools in your work, studies, or personal projects (whether you’re an analyst, content creator, marketing specialist, student, or technology enthusiast) and want to elevate your AI interactions to the next level without programming, this course will equip you with the essential and advanced skills you need.
Understand what prompt engineering is and why it guides LLM quality: impacts, use cases, and key variables like temperature, context, and model.
We break down context, role, instructions, examples, and format. Learn to combine them to precisely control tone, length, and structure of responses.
Explore Zero-shot, Few-shot, Chain-of-Thought, and Tree-of-Thought: when to use them, how to iterate quickly, and simple metrics to evaluate clarity, relevance, and cost. Definition of the methodology to use during the course.
Master how to guide the model without examples, with one, or with few; analyze accuracy, hallucinations, and cost, and apply templates for classification, generation, and extraction.
Learn to define roles (advisor, expert, salesperson) and user personas to shape tone, knowledge, and objectives; includes system and context prompt tricks across multiple platforms.
Practice step-by-step and branched reasoning for complex problems; use auto chains, node validation, and voting to improve accuracy and robustness.
Integrate external information via Retrieval-Augmented Generation: embeddings, semantic search, and metadata filters to enrich responses without retraining the model and reduce hallucinations.
Design prompts that force the model to return JSON, tables, or YAML using function calling and validators; ensure consistent formatting to connect with APIs, dashboards, and no-code flows.
Build A/B tests and regression suites: measure relevance, factuality, and cost with checklists and spreadsheets; use automated evaluators (e.g., G-Eval) and human-in-the-loop to iterate and version prompts.
Customize models without code: upload examples in visual interfaces (OpenAI Custom GPT, Azure Studio), adjust style and domain with LoRA behind the scenes, and validate improvements via interactive dashboards.
Detect and mitigate bias, PII, and harmful content; configure the Moderation API, red-teaming, and context filters; review legal frameworks (GDPR, Chilean Law 21.521) and responsible AI principles.
Instructor with over 10 years of experience in digital development. He has led around 35 AI projects in the last 5 years of his career, including chatbots, voicebots, and NLP automation and speech analytics processes, among others.
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