AI Prompting Fundamentals | Nebius Academy

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AI for Business

Available Programs

All programs AI for Managers AI for Smart Work AI for Marketing Change Management AI Prompting Essentials AI for Sales AI Automation

Are you ready to master
the skills of the future?

Uncover the Anchor Skills that will define success in 2030.

Download free eBook

AI for Tech

Available Programs

All programs Basics of Machine Learning Machine Learning in Business Numerical Methods of Machine Learning Supervised Learning Time Series in Machine Learning Linear Algebra AI-Assisted Programming

From pilots to real AI adoption

Practical guide to scale AI across your organization.

Download free eBook

English

English Español

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Modules Overview

You’ll use accessible tools like ChatGPT, Claude, and Gemini to write clearer prompts, refine outputs, and understand how LLMs generate text.

4 Modules

Module 1. Basics of AI and prompt engineering

Learn the essentials of how AI, generative AI, and LLMs actually work, when they can support daily tasks, and how to guide them through simple but effective prompts.

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Outcomes

By the end of this module, you will be able to:

Understand core GenAI and LLM concepts, model logic, myths, and limitations.
Explain in clear terms what AI, generative AI, and LLMs are—and how a model produces an answer.
Identify which tasks are suitable for AI support and which should remain fully human-driven.
Write clear prompts with goals, context, and output structure, and refine them based on results.
Recognize key risks such as factual errors, bias, and privacy issues, and apply basic verification practices.

Module 2. Limitations and Potential of Large Language Models

Deepen your understanding of how LLMs work behind the scenes—how they process language, where their limits appear, and how different methods can extend what they’re capable of in real business contexts.

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Outcomes

By the end of this module, you will be able to:

Explain how an LLM processes text (tokens, prediction, context windows) and why it may lose context or fail on longer tasks.
Compare key approaches to extending model capability—prompt engineering, fine-tuning, and RAG—and choose the right one for a given scenario.
Describe multimodal features (text + images/audio/code) and identify practical, non-technical use cases.
Evaluate and select models or tools using critical criteria such as model type, context length, cost, privacy, and security requirements.

Module 3. Prompt engineering

Learn how to design clear, structured, and reliable prompts using different prompt types, practical techniques, and verification workflows that improve the accuracy and safety of AI-generated outputs.

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Outcomes

By the end of this module, you will be able to:

Define precise goals, context, and output formats to shape high-quality model responses.
Use open-ended, closed-ended, and task-specific prompts appropriately depending on the scenario.
Apply zero-shot and few-shot techniques to improve clarity, structure, and relevance.
Identify and correct common prompt issues such as vagueness, missing context, or overloaded instructions.
Apply verify-and-edit workflows and follow basic ethical and data-sharing rules when working with AI.

Module 4. Best practices for prompt engineering

Learn advanced prompting methods to manage complex, multi-step tasks with clearer reasoning, stronger control over outputs, and more reliable results across business scenarios.

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Outcomes

By the end of this module, you will be able to:

Apply iterative refinement loops to improve prompts based on model feedback.
Use chain-of-thought prompting to request step-by-step reasoning when needed.
Break down broad business problems into sequential steps using prompt chaining.
Apply Tree of Thoughts to generate and select between multiple solution paths while keeping human oversight.

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AI Prompting Essentials

Key Program Takeaways

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Modules Overview

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