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 |
Comments