Course 'Prompt Engineering': How Learning with AI on ASI Biont Changes the Approach to Working with Neural Networks

## Introduction Imagine: you give a task to a neural network, and it produces something you didn't need. Sound familiar? In 2026, the ability to formulate queries correctly is not just a skill, but a necessity. Prompt Engineering is the art and science of creating precise instructions for language models. On the ASI Biont platform, we offer a completely free course that will teach you how to control GPT, Claude, and other models using advanced techniques. In this article, we'll break down key methods and learn how learning with AI helps you master them faster. ## Key Prompt Engineering Techniques ### Zero-shot and Few-shot: From Simple to Complex **Zero-shot** is when you give a task without examples. For example: "Translate into English: 'Prompt Engineering course'." The model handles it, but the result may not be ideal. **Few-shot** — you provide 2-3 examples. This sharply increases accuracy. Example: - Question: "What is the capital of France?" → Answer: "Paris" - Question: "What is the capital of Japan?" → Answer: "Tokyo" - Question: "What is the capital of Brazil?" → Answer: "Brasília" In the course, you'll learn to choose the right number of examples and format them for maximum effectiveness. ### Chain-of-Thought: Logic Step by Step **Chain-of-Thought (CoT)** — a technique where you ask the model to reason out loud. For example, instead of the direct question "What is 23 * 47?" you write: "Solve step by step: first multiply 23 by 40, then by 7, and add the results." This reduces errors in complex tasks by 30-40%. ### RAG and Structured Output: Working with Data **RAG (Retrieval-Augmented Generation)** — a method where the model accesses external sources (knowledge bases, documents). You learn to incorporate context: "Using data from the attached file, answer the question..." **Structured output** — a query that returns data in a strict format (JSON, table, list). Example: "Present information about the planets in a table: name, diameter, distance from the Sun." In the course, you'll master how to define structure through prompts. ## Token Optimization: Saving Resources Tokens are units of text that the model processes. The longer the prompt, the more tokens are consumed. Practical tips: - Remove unnecessary words - Use short but precise phrasing - For repetitive queries, use templates For example, instead of "Please write a summary of this article, highlighting the main ideas, and add examples," it's better to write: "Article summary: 3 main ideas + 1 example." Savings of up to 40% in tokens. ## How Learning with AI on ASI Biont Accelerates Progress The course 'Prompt Engineering' on ASI Biont is not just theory. You practice on real tasks: from creating chatbots to automating reports. AI-generated lessons adapt the material to your level — from beginner to expert. You immediately see how the model's response changes depending on the technique. ## Conclusion Prompt Engineering is the key to effective work with AI. By mastering Zero-shot, Few-shot, Chain-of-Thought, RAG, and structured output, you can solve tasks 2-3 times faster. And with the free course on ASI Biont, this path becomes accessible to everyone. Start learning right now — registration on the platform is open 24/7.

Course 'Prompt Engineering': How Learning with AI on ASI Biont Changes the Approach to Working with Neural Networks

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