Prompt Engineering: How AI is Transforming Learning on ASI Biont

## Introduction: The Art of Asking Questions to a Machine In 2026, the ability to formulate queries for neural networks has become not just a useful skill, but an essential productivity tool. **Prompt Engineering** is a discipline at the intersection of linguistics, logic, and machine learning that teaches us how to communicate effectively with AI. On the **ASI Biont** platform, we see how this skill transforms the process of **learning with AI**: students not only receive answers but learn to construct a dialogue with the system to extract maximum benefit. In this article, we will break down the key techniques of **prompt engineering** that underpin modern education. ## What is Prompt Engineering and Why is it Important? Prompt engineering is not just about writing queries; it is a complete methodology for managing the behavior of language models. Without a well-crafted prompt, even the most powerful neural network (GPT-4o, Claude 3.5, or Gemini) will produce superficial or incorrect results. The **Prompt Engineering course** on ASI Biont teaches strategies that turn AI into a personal assistant for any topic—from writing code to analyzing scientific articles. ### Why is it Relevant for Learning? - **Time savings**: a well-constructed prompt reduces iterations from 10 to 1-2. - **Depth of understanding**: AI can explain complex concepts at different levels of abstraction. - **Quality control**: you control the style, tone, and structure of the response. ## Key Prompt Engineering Techniques In the modern **Prompt Engineering course**, five key approaches are studied. Let's look at each with examples. ### 1. Zero-shot: Query Without Examples The simplest technique, where you give a task without prior demonstrations. AI uses its training data to respond. **Example:** ``` User: Explain the difference between supervised and unsupervised learning. AI: Supervised learning uses labeled data... ``` ### 2. Few-shot: Learning from Examples You show 2-3 examples of the desired response, then ask the target question. This sharply increases accuracy. **Example:** ``` Example 1: What is a neuron? — An element of the nervous system that transmits signals. Example 2: What is a synapse? — A connection between neurons. Question: What is an axon? — ... ``` ### 3. Chain-of-Thought (CoT): Reasoning Chain A technique for tasks requiring logic. You ask AI to first outline the solution steps, then give the answer. Ideal for math, programming, and data analysis. **Example:** ``` Task: Petya had 5 apples, he gave 2 to Masha, then bought 3 more. How many apples does he have now? Reasoning: Had 5 -> gave 2 -> left 3 -> bought 3 -> now 6. Answer: 6. ``` ### 4. RAG (Retrieval-Augmented Generation): Search + Generation A hybrid method where AI first searches for information in your knowledge base, then generates a response. On **ASI Biont**, this is the foundation of **learning with AI**: the student uploads a lecture, and AI responds only based on that text, without inventing facts. **Example:** ``` Context: Lecture on bioinformatics (PDF 20 pages) Query: What sequence alignment algorithms are described in the lecture? AI: The lecture describes Smith-Waterman (local) and Needleman-Wunsch (global)... ``` ### 5. Structured Output: Formatted Output Specifying an exact response structure: JSON, tables, lists. This is important for programmers and analysts. **Example:** ``` List 3 types of prompts in Markdown format: | Type | Description | Example | |---|---|---| | Zero-shot | Without examples | "Explain..." | | Few-shot | With examples | "As in the example..." | | CoT | With reasoning | "Step 1:..." | ``` ## Token Optimization: How to Save Budget Every character in a query is a token. The cost of working with GPT-4o or Claude 3.5 depends on their number. **Prompt engineering** teaches you to write concisely but substantively. | Technique | Token Consumption | Efficiency | |-----------|------------------|------------| | Zero-shot | Low | Medium | | Few-shot | High (due to examples) | High | | CoT | Medium | Very High |

Prompt Engineering: How AI is Transforming Learning on ASI Biont

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