Education — August 2026

Дания нашла самый адекватный способ бороться с домашними заданиями сделанными в ChatGPT – перестать угадывать писал ли текст АИ и просто попросить ученика защитить его устно ¯\_(ツ)_/¯

Новые правила касаются крупных экзаменов – после сдачи ученик должен будет устно объяснить свои аргументы, источники и выводы

Вот и закончилась эпоха рефератов

https://edition.cnn.com/2026/08/07/europe/ai-cheating-measures-schools-denmark-intl-scli

Дания нашла самый адекватный способ бороться с домашними заданиями сделанными в ChatGPT – перестать угадывать писал ли текст АИ и просто попросить ученика защитить его устно ¯\_(ツ)_/¯

Новые правила касаются крупных экзаменов – после сдачи ученик должен будет устно объяснить свои аргументы, источники и выводы

Вот и закончилась эпоха рефератов

https://edition.cnn.com/2026/08/07/europe/ai-cheating-measures-schools-denmark-intl-scli

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One million years without sex fails to erase stick insects' unused biological system Scientists have made a surprising finding about how an asexually reproducing stick insect has evolved. Research led by Dr. Darren Parker, lecturer in evolutionary biology at Bangor University, studied the genes of several species of Timema (a type of stick insect), which have not engaged in sexual reproduction for 1 million years—the longest known asexual period for any insect.

The wingless stick insects are native to the far western United States and reproduce by a form of virgin birth in which an embryo develops from an unfertilized egg, bypassing the need for male fertilization. This phenomenon, known as parthenogenesis, occurs naturally in some animal species.

Because some species of Timema have reproduced without sex for so long, the scientists behind the study, published in PNAS, expected that the system that evolved to balance gene expression on the sex chromosomes would have eroded over time. But that was not the case.

Parker explained, "Males and females have a different number of X chromosomes; males have one (XY), and females have two (XX). As a result, gene expression needs to be balanced equally between them. Species have evolved a wide range of mechanisms known as 'dosage compensation' to do just this. But what happens when these mechanisms for equally sharing male and female genes are no longer needed? That's the question we set out to answer.

"This is the precise situation found in asexually reproducing stick insects. Because the females reproduce without fertilization from males, there is no need to equalize gene expression. By investigating gene expression in rare males, our study found that rather than decaying away as we expected, dosage compensation mechanisms remained fully functional despite not being needed for over a million years."

The findings challenge long-standing assumptions about how quickly biological systems decay when they are no longer under strong evolutionary pressure and offer a reminder that evolution does not always discard what it no longer needs. Source: Phys.org @EverythingScience

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There was a period of about four months when I could barely function. I stayed indoors most of the time,” he said.

He added that a friend he lived with had to take care of him during that difficult period, including cooking for him, saying he sometimes ate leftovers the following morning because he could barely take care of himself.

“It was one of the most difficult periods of my life, and I survived only by God’s grace,” he said.

Baami explained that the experience also delayed his education, as he could not return to school as planned because he was struggling to repay the debt.

🏆 Building Real-World AI Projects & Portfolio 💼

This is the stage where you transform from: 👉 AI learner → AI builder

Because companies don’t only hire people who know theory.

They hire people who can:

✅ Solve problems

✅ Build applications

✅ Deploy systems

✅ Show practical experience

🎯 Why AI Projects Are Important

Projects help you:

✅ Apply concepts practically

✅ Build confidence

✅ Strengthen problem-solving

✅ Create portfolio

✅ Crack interviews

✅ Stand out from competitors

📌 What Makes a Good AI Project?

A strong AI project should:

✅ Solve a real-world problem

✅ Have clean UI/API

✅ Use proper datasets

✅ Include deployment

✅ Be available on GitHub

🧠 Beginner AI Projects

Start simple.

📊 1. House Price Prediction App

Skills Used

• Regression

• Pandas

• Scikit-learn

• Streamlit

Features

✅ Predict house prices

✅ User input form

✅ Visualization dashboard

📧 2. Spam Email Detector

Skills Used

• NLP

• TF-IDF

• Logistic Regression

Features

✅ Detect spam emails

✅ Text preprocessing

✅ Model prediction

😀 3. Face Detection System

Skills Used

• OpenCV

• Computer Vision

Features

✅ Webcam detection

✅ Real-time face recognition

💬 4. AI Chatbot

Skills Used

• NLP

• LLM APIs

• Prompt engineering

Features

✅ Interactive conversations

✅ AI responses

✅ Memory handling

📈 Intermediate AI Projects

Now start combining multiple skills.

🎥 5. AI Video Summarizer

Skills Used

• NLP

• Speech-to-text

• Transformers

Features

✅ Extract subtitles

✅ Generate summaries

🧾 6. Resume Screening System

Skills Used

• NLP

• Text similarity

• ML classification

Features

✅ Analyze resumes

✅ Match job descriptions

🛒 7. Recommendation System

Skills Used

• Collaborative filtering

• Machine Learning

Examples

• Movie recommendations

• Product recommendations

🏥 8. Medical Diagnosis Assistant

Skills Used

• Deep Learning

• Computer Vision

• NLP

Features

✅ Analyze symptoms

✅ Detect diseases from images

🤖 Advanced AI Projects

These projects make your portfolio stand out strongly.

🧠 9. PDF Q&A Chatbot (RAG)

Skills Used

• LangChain

• LLMs

• Vector DBs

• RAG

Features

✅ Upload PDFs

✅ Ask questions from documents

✅ AI-generated answers

👨‍💻 10. AI Coding Assistant

Skills Used

• LLM APIs

• Prompt engineering

Features

✅ Generate code

✅ Explain code

✅ Fix bugs

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Skills Used

• Speech recognition

• NLP

• APIs

Features

✅ Voice commands

✅ AI conversations

✅ Task automation

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Skills Used

• AI agents

• Automation

• LLM workflows

Features

✅ Research agent

✅ Coding agent

✅ Planning agent

📂 How to Structure AI Projects

A good project structure matters.

project/ │ ├── data/ ├── notebooks/ ├── models/ ├── app/ ├── requirements.txt ├── README.md └── main.py

In the previous post, we learned what programming is and why it is the foundation of every software application. Today, let's move to the next topic.

📖 Phase 1: Programming Fundamentals

📌 Topic 2: What is Python?

Python is a high-level, interpreted, and general-purpose programming language that is known for its simple syntax and readability. It was created by Guido van Rossum and first released in 1991.

Python allows you to write powerful programs with fewer lines of code compared to many other programming languages, making it an excellent choice for beginners as well as professionals.

Why is Python So Popular?

Python is one of the most widely used programming languages because it is:

• Easy to learn and read

• Beginner-friendly

• Supports multiple programming styles

• Has a huge collection of libraries

• Works on Windows, macOS, and Linux

• Backed by a large developer community

Where is Python Used?

Python is used in many industries and applications, including:

• Artificial Intelligence (AI)

• Machine Learning

• Data Science

• Data Analysis

• Web Development

• Automation and Scripting

• Cybersecurity

• Cloud Computing

• Game Development

• Internet of Things (IoT)

Why is Python the First Choice for AI?

Most AI engineers use Python because it provides powerful libraries that make AI development much easier.

Some popular Python libraries include:

• NumPy – Numerical computing

• Pandas – Data analysis

• Matplotlib – Data visualization

• Scikit-learn – Machine Learning

• TensorFlow – Deep Learning

• PyTorch – Deep Learning

• OpenCV – Computer Vision

• Transformers – Large Language Models (LLMs)

Features of Python

✅ Simple and readable syntax

✅ Free and open source

✅ Interpreted language

✅ Object-oriented

✅ Platform independent

✅ Huge ecosystem of libraries

✅ Easy to integrate with other technologies

Python vs Other Languages

Compared to languages like C++ or Java, Python requires less code to perform the same task, making development faster and reducing the chances of errors.

For example, printing a message in Python is as simple as:

print("Hello, World!")

Output:

Hello, World!

Companies That Use Python

Many of the world's leading companies use Python, including:

• Google

• OpenAI

• Netflix

• Instagram

• Spotify

• Dropbox

• Amazon

• Microsoft

Key Takeaways

• Python is a simple, powerful, and beginner-friendly programming language.

• It is the most popular language for AI, Machine Learning, and Data Science.

• Python's rich ecosystem of libraries makes AI development faster and easier.

• Learning Python is one of the best first steps toward becoming an AI Engineer.

➡️ Double Tap ❤️ For More

Интересное перед сном:

⏹️Москвичам рассказали, где покупать арбузы и дыни

⏹️Осенние каникулы у ряда школьников в РФ будут длиннее зимних

⏹️Вода в Москве-реке прогрелась до значений моря в Крыму

⏹️Москвичи пожаловались на лагерь с бесплатными восхождениями на Эльбрус

⏹️«Сортировочная»: кошачий чек-лист

⏹️«Доктор 24»: вестибулярный аппарат можно натренировать

⏹️«Мослекторий»: почему исследуют Луну, а не Марс

Новости, которые вы могли пропустить:

⏹️Четыре человека пострадали при падении лифта с четвертого этажа в Махачкале

⏹️Егор Громадский завоевал золото чемпионата Европы по современному пятиборью

⏹️Турция передала сторонам украинского конфликта предложение о моратории на военные действия

⏹️Суд оставил под арестом Rolls-Royce блогера Лерчек до исполнения приговора

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«Парк-кувшинка» у Ивановского пруда: ГК «ПИК» благоустроит зону отдыха на 29 га в Коммунарке

В Коммунарке возле Ивановского пруда появится новое крупное место притяжения для горожан. Группа компаний «ПИК», реализующая проект жилого комплекса «Бунинская набережная», создаст там городской парк. Как сообщает пресс-служба правительства Москвы, работы по благоустройству начнутся уже этой осенью.

Общая площадь территории составит 29 гектаров. Пространство планируется зонировать за счет естественного рельефа местности, создав разноуровневые площадки с разными сценариями досуга:

* Инфраструктура. На территории установят беседки, детские городки, качели и павильон для занятий йогой и медитацией. Для владельцев животных оборудуют специальную площадку. * Отдых у воды. Архитектурной доминантой парка станет необычный павильон в виде кувшинки. У береговой линии создадут зону отдыха площадью 400 кв. м. * Ландшафтный дизайн. По всей территории высадят деревья и кустарники, подобранные так, чтобы они цвели в разные месяцы года, а осенью окрашивали листву в яркие цвета. Также здесь разобьют птичий сад.

Проект комплексного развития территории «Бунинская набережная» включает возведение шести кирпично-монолитных домов высотой 16 этажей. В состав ЖК также войдут собственная школа и детский сад. Ближайшей станцией метро для жителей нового района станет «Потапово».

𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝗲𝗿: You have 2 minutes to solve this Excel problem.

You have the following data: Employee | Department | Salary John | IT | 75,000 Sarah | HR | 60,000 Mike | IT | 82,000 David | Finance | 90,000 Alice | HR | 65,000

How would you find the employee with the highest salary in the IT department?

𝗠𝗲: Challenge accepted! 💪

=XLOOKUP( MAXIFS(C2:C6,B2:B6,"IT"), C2:C6, A2:A6 )

💡 Explanation: This formula combines MAXIFS() and XLOOKUP() to find the employee with the highest salary within a specific department.

MAXIFS() finds the highest salary where the department is IT. XLOOKUP() searches for that salary in the Salary column. It returns the corresponding employee name.

This challenge tests your understanding of: ✅ MAXIFS() ✅ XLOOKUP() ✅ Multiple Criteria ✅ Combining Excel Functions

🎯 Expected Output Example Department | Highest Salary | Employee IT | 82,000 | Mike

🚀 Bonus (Dynamic Department) If cell E2 contains the department name:

=XLOOKUP( MAXIFS(C2:C6,B2:B6,E2), C2:C6, A2:A6 )

Now you can change E2 to HR, Finance, or another department and get the corresponding highest-paid employee.

⚠️ Interview Tip: If two employees have the same highest salary, XLOOKUP() returns the first matching employee. Be ready to explain how you would modify the formula if the interviewer wants all employees tied for the highest salary.

❤️ React with ❤️ for more Excel interview challenges!

Российских студентов начали заставлять скачивать госмессенджер MAX сразу после поступления, чтобы получать необходимую учебную информацию. @ruble30

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🎓🤖 Top AI Tools for Students in 2026

1. Learning & Studying

ChatGPT – Learning, explanations, coding, brainstorming, and study assistance

Google ChatGPT – Research, learning, writing, and analyzing study materials

Claude – Writing, analysis, coding, and understanding complex topics

NotebookLM – Analyzes your PDFs, notes, and study materials and answers questions about them

Quizlet – Creates flashcards, quizzes, and personalized study materials

2. Research & Papers

Perplexity – AI-powered research with web sources and citations

Elicit – Helps research academic papers and extract useful information

Consensus – Finds and summarizes research-based answers from scientific papers

Scite – Helps analyze and evaluate academic research and citations

3. Writing & Productivity

Microsoft Copilot – AI assistance for research, writing, productivity, and Microsoft tools

Grammarly – Improves grammar, writing style, clarity, and tone

DeepL – AI-powered translation and writing assistance

Otter.ai – Transcribes lectures and conversations into searchable notes

4. Math, Science & Coding

Wolfram|Alpha – Solves mathematical, scientific, and computational problems

Photomath – Solves mathematical problems using your camera and provides explanations

Desmos – Interactive graphing and mathematical visualization

GitHub Copilot – Helps students write, understand, and debug code

5. Design & Presentations

Canva AI – Creates presentations, posters, graphics, and visual content

Gamma – Creates presentations, documents, and webpages using AI

6. Other

ElevenLabs – Generates realistic AI voices and converts text into speech

Double Tap ❤️ For More

Time Complexity: O(n + m)

Space Complexity: O(n + m)

1️⃣8️⃣9️⃣ How Do You Check if Two Strings are Anagrams?

Answer:

Two strings are anagrams if they contain the same characters with the same frequencies, but possibly in a different order.

Example:

"listen" → "silent"

Both contain the same characters, so they are anagrams.

Python:

str1 = "listen" str2 = "silent"

if sorted(str1) == sorted(str2): print("Anagrams") else: print("Not Anagrams")

Time Complexity: O(n log n)

A frequency-count approach can achieve O(n) average time.

1️⃣9️⃣0️⃣ How Do You Find the First Non-Repeating Character?

Answer:

Count the frequency of every character, then scan the string again and return the first character whose frequency is "1".

Example:

Input: "swiss"

Output: "w"

Python:

from collections import Counter

text = "swiss" count = Counter(text)

for char in text: if count[char] == 1: print(char) break

Time Complexity: O(n)

Space Complexity: O(k), where "k" is the number of distinct characters.

🔥 Double Tap ❤️ For Part-20

🚀 Coding Interview Questions with Answers (Part 19)

1️⃣8️⃣1️⃣ How Do You Reverse a String?

Answer:

Reversing a string means arranging its characters in the opposite order.

Example:

Input: "hello"

Output: "olleh"

Python:

text = "hello" reversed_text = text[::-1] print(reversed_text)

Time Complexity: O(n)

Space Complexity: O(n)

1️⃣8️⃣2️⃣ How Do You Find the Largest Element in an Array?

Answer:

Traverse the array while keeping track of the largest value found so far.

Example:

Input: [10, 25, 7, 42, 18]

Output: 42

Python:

numbers = [10, 25, 7, 42, 18]

largest = numbers[0]

for num in numbers: if num > largest: largest = num

print(largest)

Time Complexity: O(n)

Space Complexity: O(1)

1️⃣8️⃣3️⃣ How Do You Find the Second Largest Element in an Array?

Answer:

Maintain two variables: one for the largest element and another for the second largest. Update them while traversing the array.

Example:

Input: [10, 25, 7, 42, 18]

Output: 25

Python:

numbers = [10, 25, 7, 42, 18]

largest = second = float('-inf')

for num in numbers: if num > largest: second = largest largest = num elif largest > num > second: second = num

print(second)

Time Complexity: O(n)

Space Complexity: O(1)

1️⃣8️⃣4️⃣ How Do You Check Whether a String is a Palindrome?

Answer:

A palindrome is a string that reads the same forward and backward.

Examples:

"madam" → Palindrome

"level" → Palindrome

"hello" → Not a palindrome

Python:

text = "madam"

if text == text[::-1]: print("Palindrome") else: print("Not a palindrome")

Time Complexity: O(n)

1️⃣8️⃣5️⃣ How Do You Find Duplicate Elements in an Array?

Answer:

Use a set to keep track of elements that have already appeared. If an element is already present in the set, it is a duplicate.

Example:

Input: [1, 2, 3, 2, 4, 1]

Output: [1, 2]

Python:

numbers = [1, 2, 3, 2, 4, 1]

seen = set() duplicates = set()

for num in numbers: if num in seen: duplicates.add(num) else: seen.add(num)

print(duplicates)

Average Time Complexity: O(n)

Space Complexity: O(n)

1️⃣8️⃣6️⃣ How Do You Remove Duplicates from an Array?

Answer:

A common approach is to use a set, which stores only unique values.

Example:

Input: [1, 2, 2, 3, 3, 4]

Output: [1, 2, 3, 4]

Python:

numbers = [1, 2, 2, 3, 3, 4]

unique_numbers = list(set(numbers))

print(unique_numbers)

If the original order must be preserved:

unique_numbers = list(dict.fromkeys(numbers))

Average Time Complexity: O(n)

1️⃣8️⃣7️⃣ How Do You Find the Missing Number in an Array?

Answer:

If an array contains numbers from "1" to "n" with one number missing, calculate the expected sum and subtract the actual sum.

Example:

Input: [1, 2, 4, 5]

Output: 3

Python:

numbers = [1, 2, 4, 5] n = 5

expected = n * (n + 1) // 2 missing = expected - sum(numbers)

print(missing)

Time Complexity: O(n)

Space Complexity: O(1)

1️⃣8️⃣8️⃣ How Do You Merge Two Sorted Arrays?

Answer:

Use two pointers to compare elements from both arrays and add the smaller element to the result.

Example:

Input:

[1, 3, 5]

[2, 4, 6]

Output:

[1, 2, 3, 4, 5, 6]

Python:

a = [1, 3, 5] b = [2, 4, 6]

i = j = 0 result = []

while i < len(a) and j < len(b): if a[i] < b[j]: result.append(a[i]) i += 1 else: result.append(b[j]) j += 1

while i < len(a): result.append(a[i]) i += 1

while j < len(b): result.append(b[j]) j += 1

print(result)

🎓🤖 Top AI Tools for Students in 2026

1. ChatGPT – Learning, explanations, coding, brainstorming, and study assistance.

2. Google Gemini – Research, learning, writing, and analyzing study materials.

3. Perplexity – AI-powered research with web sources and citations.

4. NotebookLM – Analyzes your PDFs, notes, and study materials and answers questions about them.

5. Claude – Writing, analysis, coding, and understanding complex topics.

6. Microsoft Copilot – AI assistance for research, writing, productivity, and Microsoft tools.

7. Canva AI – Creates presentations, posters, graphics, and visual content.

8. Gamma – Creates presentations, documents, and webpages using AI.

9. Quizlet – Creates flashcards, quizzes, and personalized study materials.

10. Wolfram|Alpha – Solves mathematical, scientific, and computational problems.

11. GitHub Copilot – Helps students write, understand, and debug code.

12. Grammarly – Improves grammar, writing style, clarity, and tone.

13. Otter.ai – Transcribes lectures and conversations into searchable notes.

14. Elicit – Helps research academic papers and extract useful information.

15. Consensus – Finds and summarizes research-based answers from scientific papers.

16. Scite – Helps analyze and evaluate academic research and citations.

17. DeepL – AI-powered translation and writing assistance.

18. Photomath – Solves mathematical problems using your camera and provides explanations.

19. Desmos – Interactive graphing and mathematical visualization.

20. ElevenLabs – Generates realistic AI voices and converts text into speech.

Double Tap ❤️ For More

Принято считать, что природные катастрофы не выбирают жертв. Ураган, цунами, землетрясение — одинаковая угроза для всех. Но данные говорят об обратном: одни и те же катастрофы убивают женщин и мужчин в принципиально разных пропорциях. И причина этого неравенства — не природа, а общество.

Апрельский циклон в Бангладеш в 1991 году унес около 140 000 жизней. 90% погибших составляли женщины. Среди женщин в возрасте 20–44 лет смертность достигала 71 на 1000 человек — против 15 на 1000 у мужчин того же возраста.

Цунами 2004 года в Индийском океане: женщины составили 70–80% от числа погибших. Циклон Наргис в Мьянме в 2008 году: 61% жертв — женщины.

Во время урагана Катрина около 80% людей, оставшихся в Новом Орлеане после объявления обязательной эвакуации, составляли женщины — при том что они составляют лишь 54% населения города. Это устойчивая закономерность, зафиксированная в десятках стран.

Почему так происходит

Ключевое исследование по этой теме — работа Ноймайера и Плюмпера, охватившая данные 141 страны за период 1981–2002 годов. Авторы установили, что биологические и физиологические различия между полами не объясняют масштабных гендерных различий в смертности. Главная причина — социально сконструированная гендерная уязвимость женщин, встроенная в повседневные социально-экономические паттерны. Конкретные механизмы этой уязвимости хорошо задокументированы.

Во-первых, ответственность за детей и пожилых. По данным Oxfam, в пострадавших от цунами 2004 года регионах женщины гибли, потому что оставались разыскивать детей и других родственников. В Бангладеше среди задокументированных причин более высокой смертности женщин — попытка матери спасти детей ценой собственной жизни.

Во-вторых, запрет на определенные навыки. В пострадавших районах Шри-Ланки плавание и лазание по деревьям считались занятиями «почти исключительно для мужчин» — именно эти навыки помогли мужчинам выжить в волнах. В сельском Бангладеше социальный предрассудок против обучения женщин плаванию резко снижал их шансы на выживание при наводнении.

В-третьих, ограничение подвижности. В сельском Бангладеше от женщин ожидалось ношение сари — одежды, затрудняющей бег и плавание, — и они не могли покидать дом без разрешения мужа, отца или брата. Эти нормы препятствовали их перемещению и доступу к информации об угрозе.

В-четвертых, информационное неравенство. Во время циклона в Бангладеше в 1991 году мужчины активно и самостоятельно собирали предупреждения об угрозе, тогда как женщины полагались преимущественно на слухи — и часть из них вовсе не знала о приближающемся циклоне.

В-пятых, само место пребывания в момент удара. Во время землетрясений мужчины с большей вероятностью находятся на улице или в более прочных производственных и общественных зданиях, тогда как женщины — дома, в жилых постройках, которые рушатся в первую очередь.

Чем выше социально-экономический статус женщин в стране, тем слабее проявляется гендерный разрыв в смертности от катастроф. Иными словами, там, где женщины имеют доступ к образованию, ресурсам, информации и праву принимать решения самостоятельно, они выживают наравне с мужчинами. Это делает гибель женщин в катастрофах не неизбежной природной трагедией, а предсказуемым и предотвратимым последствием гендерного неравенства.

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ПОДПИСКА | БОТ | ЧАТ

🚀 Generative AI Fundamentals – Part 5

🔎 Embeddings, Vector Databases, Semantic Search & RAG Deep Dive

These concepts are the backbone of modern enterprise GenAI applications. Most LLM Engineer and GenAI interviews include questions on them.

1. Why do LLMs need external knowledge?

LLMs are trained on historical data and have limitations:

Knowledge becomes outdated

Cannot access private company documents by default

May hallucinate

Cannot answer questions about new information unless connected to external data

Example: If a company's HR policy changes today, the LLM won't know it unless it retrieves the latest document.

This is why RAG (Retrieval-Augmented Generation) is widely used.

2. What are Embeddings?

Embeddings are numerical vector representations of text that capture semantic meaning.

Instead of storing text directly, AI converts it into vectors.

Example

Cat → [0.32, 0.45, 0.87...]

Dog → [0.31, 0.47, 0.85...]

Car → [0.91, 0.12, 0.44...]

Notice that Cat and Dog have similar vectors because their meanings are related.

3. Why are Embeddings Important?

Embeddings allow AI to understand meaning, not just exact words.

Applications:

Semantic Search

Recommendation Systems

RAG

Duplicate Detection

Document Clustering

Similarity Search

4. What is a Vector Database?

A Vector Database stores embeddings instead of plain text.

It enables fast similarity searches across millions of vectors.

Popular Vector Databases:

Pinecone

Chroma

Weaviate

FAISS

Milvus

Qdrant

These databases are optimized for vector similarity search rather than traditional SQL queries.

5. Traditional Search vs Semantic Search

Traditional Search:

Matches keywords

Exact words required

Limited context

Less accurate

Semantic Search:

Matches meaning

Understands intent

Context-aware

More relevant results

Example

Search: "How to lose weight"

Semantic search may also return:

Fat loss tips

Weight reduction strategies

Healthy diet plans

Even if the exact words don't match.

6. What is Vector Similarity Search?

Vector similarity search finds documents whose embeddings are closest to the query embedding.

Workflow

User Query

↓

Generate Query Embedding

↓

Compare with Stored Embeddings

↓

Find Most Similar Documents

↓

Return Results

Common similarity metrics:

Cosine Similarity

Euclidean Distance

Dot Product

7. What is RAG (Retrieval-Augmented Generation)?

RAG combines:

Information Retrieval

Large Language Models

Instead of relying only on the model's memory, RAG retrieves relevant information before generating an answer.

8. How does a RAG pipeline work?

User Question

↓

Embedding Model

↓

Vector Database

↓

Similarity Search

↓

Relevant Documents

↓

LLM

↓

Final Answer

Example:

Question: "What is our company's leave policy?"

The system:

1. Retrieves the HR policy document.

2. Sends the relevant section to the LLM.

3. Generates an accurate answer based on that document.

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Дома, школы, детские сады, больницы, дороги, мосты, спортивные объекты — за всем этим стоит ежедневный труд строителей. Именно благодаря вам меняются города, появляются новые возможности для миллионов людей.

Сегодня по всей стране строят и обновляют социальные объекты, в том числе по Народной программе.

Спасибо всем, кто посвятил себя этой профессии! Желаем крепкого здоровья, благополучия, новых успехов и самых смелых проектов. Пусть результаты вашего труда долгие годы служат людям!

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