Vijay kumar 🧑‍💻

I love sports n fun things🧑‍💻 🏋️ 🏃 🤾 🏌️🤼
RAN performance Engineer 📡🛰️

[Vijay kumar 🧑‍💻 @_vijaykr]

Top Prompt Engineering Interview Questions
1. What is Prompt Engineering?
Prompt engineering is the process of designing and optimizing prompts (instructions) given to AI models to generate accurate, relevant, and useful outputs.
A prompt may include
✔ Instructions
✔ Context
✔ Examples
✔ Constraints
✔ Output format

*✅ Example:*
Instead of:
“Write about AI”

Use:
“Explain Generative AI in simple language with 3 real-world examples for beginners.”

*2. Why is Prompt Engineering Important in AI?*
Prompt engineering is important because AI models depend heavily on the quality of the input they receive.

*A good prompt helps:*
✔ Improve accuracy
✔ Reduce hallucinations
✔ Control tone and style
✔ Generate structured outputs
✔ Improve reasoning
✔ Save time and cost

✅ Better prompts = Better AI responses.

*3. Difference Between Prompt Engineering and Traditional Programming*
*🖥 Traditional Programming:*
- Uses fixed rules and logic
- Deterministic outputs
- Requires coding syntax

*🤖 Prompt Engineering:*
- Uses natural language instructions
- AI interprets intent dynamically
- Probabilistic outputs

*✅ Example:*
*Traditional Code:*
if marks > 40:
print("Pass")
*Prompt Engineering:*
“Determine whether the student passed based on marks greater than 40.”

*4. How Does Prompt Engineering Affect Model Behavior?*
Prompt engineering directly influences:
✔ Accuracy
✔ Creativity
✔ Tone
✔ Structure
✔ Reasoning depth
✔ Safety behavior

Even small wording changes can produce very different outputs.

*✅ Example:*
*Prompt 1:*
“Explain Python.”

*Prompt 2:*
“Explain Python for a 10-year-old using simple analogies.”

The second prompt changes the complexity and tone completely.

*5. What Makes a Prompt “Good”?*
A good prompt is:
✔ Clear
✔ Specific
✔ Context-aware
✔ Structured
✔ Goal-oriented

*✅ Bad Prompt:*
“Tell me about databases.”

*✅ Good Prompt:*
“Explain relational databases with examples of MySQL and PostgreSQL in under 200 words.”

*6. What Are the Basic Components of a Prompt?*
*🧩 Main Components:*
*1️⃣ Instruction*
→ What the AI should do

*2️⃣ Context*
→ Background information

*3️⃣ Input Data*
→ Content to process

*4️⃣ Output Format*
→ Desired response structure

*5️⃣ Constraints*
→ Rules or limitations

*✅ Example:*
“Summarize this article in 5 bullet points using simple English.”

*7. What is Context in a Prompt?*
Context is the background information provided to help the AI understand the task more accurately.

*It improves:*
✔ Relevance
✔ Accuracy
✔ Personalization
✔ Consistency

*✅ Example:*
*Without context:*
“Write an email.”

*With context:*
“Write a professional leave request email for a 3-day medical leave.”

*8. What is the “Instruction” in a Prompt?*
The instruction is the command that tells the AI what action to perform.

*Examples:*
✔ Explain
✔ Summarize
✔ Translate
✔ Analyze
✔ Compare
✔ Generate

*✅ Example:*
“Translate this paragraph into French.”
Here, “Translate” is the instruction.

*9. How Do You Measure Prompt Quality?*
Prompt quality is measured using:
✔ Accuracy
✔ Relevance
✔ Completeness
✔ Consistency
✔ Formatting correctness
✔ Hallucination rate
✔ User satisfaction

✅ A high-quality prompt consistently produces reliable results with minimal retries.

*10. Why is Prompt Engineering Called “The New Programming”?*
Prompt engineering is called “the new programming” because prompts are becoming a way to control AI systems just like code controls software.

Instead of writing strict logic, developers now guide AI behavior using natural language instructions.
Example
“Act as a customer support assistant and answer politely.”

[Vijay kumar 🧑‍💻 @_vijaykr]

Generative AI & Large Language Models LLMs

After learning:
✅ Python Fundamentals
✅ Data Handling
✅ Visualization
✅ Statistics
✅ Machine Learning
✅ Deep Learning
✅ NLP
✅ Computer Vision

now comes the most trending and revolutionary field in AI:

Generative AI & LLMs

This technology powers:
- ChatGPT
- AI image generators
- AI coding assistants
- AI voice tools
- AI video creation
- Smart AI agents

What is Generative AI?
Generative AI is a branch of AI that creates new content such as:
✅ Text
✅ Images
✅ Audio
✅ Video
✅ Code

Instead of only analyzing data, Generative AI can generate completely new outputs.

Why Generative AI is Important
Generative AI is transforming industries:
- Education
- Healthcare
- Marketing
- Software Development
- Design
- Content Creation
It helps automate creative and intelligent tasks at massive scale.

What are Large Language Models LLMs?
LLMs are advanced AI models trained on massive text datasets.

*They understand:*
- Language
- Context
- Patterns
- Meaning

*Popular LLMs*
- ChatGPT
- Gemini
- Claude
- Llama

How LLMs Work
LLMs predict the next word/token based on context.

*Simple Example*
*Input:*
> “AI is transforming…”

*Prediction:*
> “the world”

This prediction process happens billions of times during training.

Transformer Architecture
Modern LLMs are based on Transformers.

*Transformers changed AI completely because they:*
✅ Understand context better
✅ Handle large text efficiently
✅ Scale effectively

Attention Mechanism
Attention helps models focus on important words.

*Example*
*Sentence:*
> “The cat chased the mouse because it was hungry.”

*Attention helps identify:*
“it” refers to the cat.

Important Generative AI Tools
*1. OpenAI APIs*
Used for:
- Chatbots
- AI assistants
- Text generation

*2. Hugging Face*
Popular platform for:
- LLMs
- AI models
- Transformers

*Website*
Hugging Face

*3. LangChain*
Framework for building LLM applications.

*Used For*
- AI agents
- Chatbots
- RAG systems

*4. LlamaIndex*
Used for:
- Connecting LLMs with documents/data

Prompt Engineering
One of the most important Generative AI skills.

Prompt engineering means designing effective prompts to get better AI responses.

*Bad Prompt*
> “Tell me about AI”

Good Prompt
> “Explain Artificial Intelligence in simple terms for beginners with real-world examples.”

Types of Prompting
*1. Zero-Shot Prompting*
No examples provided.

*2. Few-Shot Prompting*
Examples are included.

*3. Chain-of-Thought Prompting*
Encourages step-by-step reasoning.

RAG — Retrieval-Augmented Generation*
RAG combines:
- Search systems
- LLMs

How It Works
1. Retrieve relevant information
2. Send context to LLM
3. Generate better answers

Vector Databases
LLMs use embeddings stored in vector databases.

*Popular Vector DBs*
- Pinecone
- ChromaDB
- Weaviate

Embeddings
Embeddings convert:
- Text
- Images
- Data

into numerical vectors.

These vectors help AI understand similarity and meaning.

AI Image Generation
Generative AI can create images from prompts.

*Popular Models*
- DALL·E
- Stable Diffusion

AI Audio & Music Generation
AI can generate:
✅ Voiceovers
✅ Songs
✅ Background music
✅ Sound effects

AI Agents
AI agents are autonomous systems that:
- Think
- Plan
- Use tools
- Complete tasks

*Examples*
- AI coding agents
- AI research assistants
- Automation bots

[Vijay kumar 🧑‍💻 @_vijaykr]

Computer Vision for Beginners
After learning:
✅ Python Fundamentals
✅ Data Handling
✅ Visualization
✅ Statistics
✅ Machine Learning
✅ Deep Learning
✅ NLP
the next exciting AI field is:
Computer Vision
Computer Vision helps machines understand and analyze images and videos just like humans.
It powers:
- Face recognition
- Self-driving cars
- Medical imaging
- Security systems
- Object detection
- AI cameras
What is Computer Vision?
Computer Vision is a branch of AI that enables computers to:
✅ Understand images
✅ Detect objects
✅ Analyze videos
✅ Recognize faces
✅ Process visual information
Why Computer Vision is Important
Today massive amounts of visual data are generated daily:
- Photos
- Videos
- CCTV footage
- Medical scans
Computer Vision helps AI systems process this visual information automatically.
Popular Computer Vision Libraries
1. OpenCV
Most popular Computer Vision library.
Used for:
- Image processing
- Face detection
- Video analysis

*2. TensorFlow / PyTorch*
Used for:
- Deep Learning vision models
- CNN training

*3. YOLO*
Popular real-time object detection system.

Install OpenCV*
pip install opencv-python
*🖼️ 1. Reading Images in Python*
import cv2

image = cv2.imread("image.jpg")

cv2.imshow("Image", image)

cv2.waitKey(0)
2. Image Processing Basics*
Computer Vision systems often preprocess images before analysis.

*Common Operations*
✅ Resize images
✅ Crop images
✅ Blur images
✅ Convert colors
✅ Edge detection

*Resize Image Example*
resized = cv2.resize(image, (300,300))
*🌈 3. Convert Image to Grayscale*
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
*Why Important?*
Reduces complexity and improves processing speed.

4. Edge Detection*
Helps identify object boundaries.
edges = cv2.Canny(gray, 100, 200)
*Applications*
- Lane detection
- Shape recognition
- Medical imaging

5. Face Detection*
One of the most common Computer Vision tasks.

*OpenCV Face Detection*
face_cascade = cv2.CascadeClassifier(
'haarcascade_frontalface_default.xml'
)
*Applications*
✅ Smartphone face unlock
✅ Attendance systems
✅ Security systems

6. Video Processing*
Computer Vision also processes videos frame-by-frame.
cap = cv2.VideoCapture(0)
*Applications*
- CCTV monitoring
- Traffic analysis
- Motion detection

7. CNN in Computer Vision*
CNN (Convolutional Neural Networks) are the foundation of modern Computer Vision.

*Why CNNs?*
They automatically learn:
- Edges
- Shapes
- Patterns
- Objects

* 8. Image Classification*
Classifies entire images into categories.

*Examples*
- Cat vs Dog
- Healthy vs Diseased Plant
- Car vs Bike

* 9. Object Detection*
Detects and locates multiple objects.

*Popular Models*
- YOLO
- SSD
- Faster R-CNN

* YOLO — Real-Time Object Detection*
YOLO = You Only Look Once

*Why Popular?*
✅ Extremely fast
✅ Real-time detection
✅ High accuracy

*Applications*
- Self-driving cars
- Security cameras
- Retail analytics

*10. Computer Vision in Healthcare*
Computer Vision is transforming healthcare.

*Applications*
✅ X-ray analysis
✅ Cancer detection
✅ MRI scan analysis
✅ Disease diagnosis

*11. Self-Driving Cars*
Computer Vision helps autonomous vehicles:
✅ Detect lanes
✅ Identify pedestrians
✅ Recognize traffic signs
✅ Avoid obstacles

*12. OCR — Optical Character Recognition*
OCR extracts text from images.

*Examples*
- Document scanners
- Number plate recognition
- Invoice readers

*Important Computer Vision Concepts*

*Image Classification* : Identify image category
*Object Detection* : Locate objects
*Segmentation* : Separate image regions
*CNN* : Deep Learning for images
*OCR* : Extract text from images

*Beginner Computer Vision Projects*

*Easy Projects*
✅ Face Detection System
✅ Image Filter App
✅ QR Code Scanner

*Intermediate Projects*
✅ Mask Detection System
✅ Object Detection App
✅ Attendance System
✅ OCR Reader

[Vijay kumar 🧑‍💻 @_vijaykr]

RCB...❤️ #IPL

[Vijay kumar 🧑‍💻 @_vijaykr]

@IndianTechGuide JSW steel plant

[Vijay kumar 🧑‍💻 @_vijaykr]

@soham_nayak04 Delhi NCR

[Vijay kumar 🧑‍💻 @_vijaykr]

@soul4tech Lol....🤣🤣🤣🤣🤣

[Vijay kumar 🧑‍💻 @_vijaykr]

@MahimaJalan2 Lala company 😭

[Vijay kumar 🧑‍💻 @_vijaykr]

2026 2.5 L 🙈🤣

[Vijay kumar 🧑‍💻 @_vijaykr]

Data Handling with Python (Most Important Step Before ML)

After learning Python fundamentals, the next major step in AI is understanding how to work with data.

Because in AI:
- Data is everything
- Models learn from data
- Better data = Better AI systems

Before jumping into:
- Machine Learning
- Deep Learning
- Neural Networks

you must learn how to:
✅ Read data
✅ Clean data
✅ Analyze data
✅ Transform data
✅ Visualize data

This process is called Data Handling.

*🧠 Why Data Handling is Important in AI*
Real-world data is usually:
- Messy
- Incomplete
- Unstructured
- Full of missing values

AI Engineers spend huge time cleaning and preparing data before training models.

👉 In real companies:
- 70% Data Handling
- 30% Model Building

That’s why mastering data handling is extremely important.

*📦 Most Important Python Libraries for Data Handling*

*1. NumPy*
Used for:
- Numerical computing
- Arrays
- Mathematical operations

*2. Pandas*
Used for:
- Data analysis
- Data cleaning
- Working with CSV/Excel files

*3. Matplotlib & Plotly*
Used for:
- Data visualization
- Graphs
- Charts

*🔢 1. NumPy Fundamentals*
NumPy is the foundation of data science and AI.
It provides powerful array operations.

*Install NumPy*
pip install numpy

*Create Arrays*
import numpy as np

arr = np.array()[1][2][3][4]

print(arr)

*Mathematical Operations*
arr = np.array()[1][2][3]

print(arr + 2)
print(arr * 2)

*Why NumPy is Important for AI?*
AI models process massive numerical data using arrays and matrices.

*📊 2. Pandas Fundamentals*
Pandas is the most important library for data analysis.

*Install Pandas*
pip install pandas

*Create DataFrame*
import pandas as pd

data = {
"Name": ["A", "B", "C"],
"Marks":
}[90][85][95]

df = pd.DataFrame(data)

print(df)

*📂 3. Reading CSV Files*
Most AI datasets come in CSV format.

df = https://t.co/GDwE4mhBkh_csv("data.csv")

print(df.head())

*🔍 4. Exploring Data*
*Check Dataset Information*
print(https://t.co/SZMwQSc6SZ())

*Check Missing Values*
print(df.isnull().sum())

*Statistical Summary*
print(df.describe())

*🧹 5. Data Cleaning*
Real-world datasets contain:
- Missing values
- Duplicate values
- Incorrect data

*Remove Missing Values*
df.dropna(inplace=True)

*Fill Missing Values*
df.fillna(0, inplace=True)

*Remove Duplicates*
df.drop_duplicates(inplace=True)

*📌 6. Filtering Data*
high_marks = df[df["Marks"] > 90]

print(high_marks)

*📈 7. Sorting Data*
df.sort_values(by="Marks", ascending=False)

*📊 8. GroupBy Operations*
Very important for analysis.

df.groupby("Department")["Salary"].mean()

*🔄 9. Merging Datasets*
merged = pd.merge(df1, df2, on="ID")

*📉 10. Data Visualization Basics*
Visualization helps understand patterns quickly.

*Install Matplotlib*
pip install matplotlib

*Simple Line Plot*
import matplotlib.pyplot as plt

x =
y =[1][2][3][10][20][30]

plt.plot(x, y)
https://t.co/MSPXcTWw2s()

*📊 Common Charts in AI*
✅ Line Chart
✅ Bar Chart
✅ Histogram
✅ Pie Chart
✅ Scatter Plot
✅ Heatmap

*🔥 Mini Projects for Practice*
*Beginner Projects*
✅ Student Marks Analysis
✅ IPL Dataset Analysis
✅ COVID Data Analysis
✅ Sales Dashboard

*Intermediate Projects*
✅ Netflix Data Analysis
✅ YouTube Trending Analysis
✅ Stock Market Analysis

*📚 Best Platforms for AI Datasets*
- Kaggle
- UCI Machine Learning Repository
- https://t.co/wb8Drm1pcV

*🎯 Skills You Must Master Before ML*
Before learning Machine Learning, become comfortable with:
✅ NumPy Arrays
✅ Pandas DataFrames
✅ Data Cleaning
✅ Handling Missing Values
✅ Filtering & Sorting
✅ GroupBy Operations
✅ Visualization
✅ CSV Handling

[Sundar Pichai @sundarpichai]

Today at the @Android Show (I/O edition) we announced Gemini Intelligence - bringing the best of Gemini to our most advanced devices.

Automate multi-step tasks across apps and Chrome, fill out forms in a single tap, turn spoken thoughts into polished text with Rambler, build custom widgets & loads more.

[Vijay kumar 🧑‍💻 @_vijaykr]

21 Generative AI Terms You Should Know-

1. Generative AI*
→ AI that creates original content like text, images, videos, music, and code.

2. Text Generation*
→ AI generating human-like written content from prompts or instructions.

3. Image Generation*
→ AI creating realistic or artistic images using text prompts.

4. Video Generation*
→ AI producing videos automatically from text, images, or scripts.

5. Code Generation*
→ AI generating programming code, functions, or software solutions.

6. Prompt*
→ Instructions given to generative AI systems to create outputs.

7. Prompt Engineering*
→ Crafting optimized prompts to improve AI-generated results.

8. LLM (Large Language Model)*
→ Large AI systems trained on huge text datasets to generate natural language responses.

9. Transformer Model*
→ The deep learning architecture powering most modern generative AI systems.

10. Token*
→ Small text units processed by AI during understanding and generation.

11. Fine-Tuning*
→ Training an existing AI model on specialized datasets for specific tasks.

12. Multimodal AI*
→ AI capable of generating and understanding multiple content types together.

13. Diffusion Model*
→ AI models commonly used for generating realistic images and videos.

14. Hallucination*
→ AI generating incorrect or fabricated information confidently.

15. Inference*
→ The process where trained AI models generate outputs for users.

16. Embeddings*
→ Numerical representations helping AI understand relationships between data and meaning.

17. Synthetic Data*
→ Artificially generated data created by AI for training or testing purposes.

18. AI Creativity*
→ AI combining learned patterns to generate new and unique outputs.

19. Content Personalization*
→ AI generating customized content based on user preferences or behavior.

20. Open Source Generative AI*
→ Publicly available generative AI models developers can modify and improve.

21. AI Copilot*
→ Generative AI assistants designed to help humans work faster and more efficiently.
Generative AI is transforming how people create, work, learn, and build businesses.

[Vijay kumar 🧑‍💻 @_vijaykr]

Latest Microsoft Copilot Updates May 2026

Copilot Cowork Mode
- Biggest 2026 update: plans & executes multi-step tasks in background, users delegate work & monitor checkpoints

Agent Mode Expansion*
- Copilot moves from chat assistant to action-taking AI agent running workflows across Microsoft 365

Copilot Notebook Grounding*
- Agents stay aligned with your references & working materials for better accuracy

Thinking Mode Selector*
- Toggle between reasoning modes like o3/o4-mini for complex tasks

Outlook Copilot Improvements*
- Anchored to the email you're viewing for higher accuracy

Security Copilot Upgrades*
- Agentic Secret Finder detects exposed credentials in emails, chats, documents & screenshots

Multi-agent Reasoning*
- Full integration with Microsoft Defender, Entra & Purview for security workflows

Copilot Dashboard Insights*
- New metrics for admins: satisfaction, intent & usage tracking across teams

Computer Use Capability*
- Automate web & desktop apps — General Availability May 2026

[Vijay kumar 🧑‍💻 @_vijaykr]

May LLMs List--

- GPT-5.5 or (“Spud”) ✅
- Meta Muse Spark ✅
- Claude 5 or (“Mythos”) - Beta Release ✅
- Claude 4.7 ✅
- Deepseek V4 Preview Pro/Flash ✅
- Grok 4.3 ✅
- Gemini 3.1 Flash Live ✅
- Laguna XS.2✅
- Xiaomi Mimo X2.5✅
- Ling-2.6-flash✅
- IBM Granite 4.1✅
- Mistral Medium✅
- Kimi K2.6 ✅
- Kimi K3.0 ❌
- Qwen3.6-Max-Preview ✅
- Qwen3.6-Plus✅
- Qwen3.5 Max Pro✅
- Qwen3.5 Omni Plus✅
- GLM-5.1 open ✅
- GLM-5V-Turbo✅
- Images 2 ✅
- MiMo-V2.5-Pro✅
- Hunyuan Hy3 Preview ✅
- Gemma 4 series✅
- Trinity Large Thinking (🇺🇸) ✅
- 1-bit Bonsai 8B (🇺🇸) ✅
- Holo3 (🇫🇷) ✅
- Tencent Hy3-preview ✅
- Sarvam 105B and Sarvam 30B (🇮🇳) ✅
- MAI-Transcribe-1, MAI-Voice/IMAGE-1 ✅
- MiniMax M2.7 open weights ✅
- MiniMax M3.0❌
- StepFun❌
- Hunyuan3 0B MoE❌

[Vijay kumar 🧑‍💻 @_vijaykr]

Complete Roadmap to Learn Generative AI

*1. Understand Basics of AI & Machine Learning*
Learn concepts like supervised/unsupervised learning, neural networks, and deep learning fundamentals.

*2. Study Generative Models*
Focus on models like GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders), and autoregressive models.

*3. Learn Deep Learning Frameworks*
Get hands-on with TensorFlow or PyTorch for building and training models.

*4. Explore Natural Language Processing (NLP)*
Understand transformers, language models like GPT, BERT, and sequence-to-sequence models.

*5. Hands-On Projects*
Build projects like image generation, text generation, style transfer, and chatbot development.

*6. Work with Pretrained Models & APIs*
Use models like OpenAI’s GPT, DALL·E, Stable Diffusion to create generative applications.

*7. Learn Ethical AI & Bias*
Study the impact, challenges, and responsible use of generative AI technologies.

*8. Keep Updated with Research*
Follow latest papers, blogs, and tutorials to stay ahead in this rapidly evolving field.

[Vijay kumar 🧑‍💻 @_vijaykr]

Nice weather..🔥❤️

[Vijay kumar 🧑‍💻 @_vijaykr]

@_itz_joe Lol...🤣🤣

[Vijay kumar 🧑‍💻 @_vijaykr]

🌧️ 🌧️

[Vijay kumar 🧑‍💻 @_vijaykr]

🥰guy's... please, stay alive for the books you haven't read, the TV show you haven't finished, and the stray cats you haven't petted yet.
stay alive for the smell of rain, your morning coffee, the songs that will become ur new favorites, and the versions of urself you haven't met yet

[Vijay kumar 🧑‍💻 @_vijaykr]

@ravikiran_dev7 128 GB