🔰 Artificial Intelligence Roadmap 🤖

1️⃣ Foundations of AI & Math Essentials
├── What is AI, ML, DL?
├── Types of AI: Narrow, General, Super AI
├── Linear Algebra: Vectors, Matrices, Eigenvalues
├── Probability & Statistics: Bayes Theorem, Distributions
├── Calculus: Derivatives, Gradients (for optimization)

2️⃣ Programming & Tools
💻 Python – NumPy, Pandas, Matplotlib, Seaborn
🧰 Tools – Jupyter, VS Code, Git, GitHub
📦 Libraries – Scikit-learn, TensorFlow, PyTorch, OpenCV
📊 Data Handling – CSV, JSON, APIs, Web Scraping

3️⃣ Machine Learning (ML)
📈 Supervised Learning – Regression, Classification
🧠 Unsupervised Learning – Clustering, Dimensionality Reduction
🎯 Model Evaluation – Accuracy, Precision, Recall, F1, ROC
🔄 Model Tuning – Cross-validation, Grid Search
📂 ML Projects – Spam Classifier, House Price Prediction, Loan Approval

4️⃣ Deep Learning (DL)
🧠 Neural Networks – Perceptron, Activation Functions
🔁 CNNs – Image classification, object detection
🗣 RNNs & LSTMs – Time series, text generation
🧮 Transfer Learning – Using pre-trained models
🧪 DL Projects – Face Recognition, Image Captioning, Chatbots

5️⃣ Natural Language Processing (NLP)
📚 Text Preprocessing – Tokenization, Lemmatization, Stopwords
📊 Vectorization – TF-IDF, Word2Vec, BERT
🧠 NLP Tasks – Sentiment Analysis, Text Summarization, Q&A
💬 Chatbots – Rule-based, ML-based, Transformers

6️⃣ Computer Vision (CV)
📷 Image Processing – Filters, Edge Detection, Contours
🧠 Object Detection – YOLO, SSD, Haar Cascades
🧪 CV Projects – Mask Detection, OCR, Gesture Recognition

7️⃣ MLOps & Deployment
☁️ Model Deployment – Flask, FastAPI, Streamlit
📦 Model Saving – Pickle, Joblib, ONNX
🚀 Cloud Platforms – AWS, GCP, Azure
🔄 CI/CD for ML – MLflow, DVC, GitHub Actions

8️⃣ Optional Advanced Topics
📘 Reinforcement Learning – Q-Learning, DQN
🧠 GANs – Generate realistic images
🔐 AI Ethics – Bias, Fairness, Explainability
🧠 LLMs – Transformers, GPT, BERT, LLaMA

9️⃣ Portfolio Projects to Build
✔️ Spam Classifier
✔️ Face Recognition App
✔️ Movie Recommendation System
✔️ AI Chatbot
✔️ Image Caption Generator

💬 Tap ❤️ for more!

Many people reached out to me saying telegram may get banned in their countries. So I've decided to create WhatsApp channels based on your interests 👇👇

Free Courses with Certificate: https://whatsapp.com/channel/0029Vamhzk5JENy1Zg9KmO2g

Jobs & Internship Opportunities:
https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226

Web Development: https://whatsapp.com/channel/0029VaiSdWu4NVis9yNEE72z

Python Free Books & Projects: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L

Java Resources: https://whatsapp.com/channel/0029VamdH5mHAdNMHMSBwg1s

Coding Interviews: https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X

SQL: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v

Power BI: https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c

Programming Free Resources: https://whatsapp.com/channel/0029VahiFZQ4o7qN54LTzB17

Data Science Projects: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y

Learn Data Science & Machine Learning: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D

Don’t worry Guys your contact number will stay hidden!

ENJOY LEARNING 👍👍

✅ 🔤 A–Z of Artificial Intelligence 🤖

This A-Z captures the essentials of 2025 AI from IBM's core definitions and DataCamp's beginner guides, spotlighting breakthroughs like transformers and GANs that drive 85% of real-world apps from chatbots to self-driving tech—perfect for grasping how AI mimics human smarts!

A – Algorithm
A step-by-step procedure used by machines to solve problems or perform tasks.

B – Backpropagation
A core technique in training neural networks by minimizing error through gradient descent.

C – Computer Vision
AI field focused on enabling machines to interpret and understand visual information.

D – Deep Learning
A subset of ML using neural networks with many layers to model complex patterns.

E – Ethics in AI
Concerns around fairness, bias, transparency, and responsible AI development.

F – Feature Engineering
The process of selecting and transforming variables to improve model performance.

G – GANs (Generative Adversarial Networks)
Two neural networks competing to generate realistic data, like images or audio.

H – Hyperparameters
Settings like learning rate or batch size that control model training behavior.

I – Inference
Using a trained model to make predictions on new, unseen data.

J – Jupyter Notebook
An interactive coding environment widely used for prototyping and sharing AI projects.

K – K-Means Clustering
A popular unsupervised learning algorithm for grouping similar data points.

L – LSTM (Long Short-Term Memory)
A type of RNN designed to handle long-term dependencies in sequence data.

M – Machine Learning
A core AI technique where systems learn patterns from data to make decisions.

N – NLP (Natural Language Processing)
AI's ability to understand, interpret, and generate human language.

O – Overfitting
When a model learns noise in training data and performs poorly on new data.

P – PyTorch
A flexible deep learning framework popular in research and production.

Q – Q-Learning
A reinforcement learning algorithm that helps agents learn optimal actions.

R – Reinforcement Learning
Training agents to make decisions by rewarding desired behaviors.

S – Supervised Learning
ML where models learn from labeled data to predict outcomes.

T – Transformers
A deep learning architecture powering models like BERT and GPT.

U – Unsupervised Learning
ML where models find patterns in data without labeled outcomes.

V – Validation Set
A subset of data used to tune model parameters and prevent overfitting.

W – Weights
Parameters in neural networks that are adjusted during training to minimize error.

X – XGBoost
A powerful gradient boosting algorithm used for structured data problems.

Y – YOLO (You Only Look Once)
A real-time object detection system used in computer vision.

Z – Zero-shot Learning
AI's ability to make predictions on tasks it hasn’t explicitly been trained on.

Double Tap ♥️ For More

🎯 50 Steps to Learn AI

🔹 Basics
1. Understand what AI is
2. Explore real-world AI use cases
3. Learn basic AI terms
4. Grasp programming fundamentals
5. Start Python for AI

🔹 Math & ML Basics
6. Learn stats & probability
7. Study linear algebra basics
8. Get into machine learning
9. Know ML learning types
10. Explore ML algorithms

🔹 First Projects
11. Build a simple ML project
12. Learn neural network basics
13. Understand model architecture
14. Use TensorFlow or PyTorch
15. Train your first model

🔹 Deep Learning
16. Avoid overfitting/underfitting
17. Clean & prep data
18. Evaluate with accuracy, F1
19. Explore CNNs & RNNs
20. Try a computer vision task

🔹 NLP & RL
21. Start with NLP basics
22. Use NLTK or spaCy
23. Learn reinforcement learning
24. Build a simple RL agent
25. Study GANs and VAEs

🔹 Cloud & Ethics
26. Create a generative model
27. Learn AI ethics & bias
28. Explore AI industry use cases
29. Use cloud AI tools
30. Deploy models to cloud

🔹 Real-World Use
31. Study AI in business
32. Match tasks to algorithms
33. Learn Hadoop or Spark
34. Analyze time series data
35. Apply model tuning techniques

🔹 Community & Portfolio
36. Use transfer learning models
37. Read AI research papers
38. Contribute to open-source AI
39. Join Kaggle competitions
40. Build your AI portfolio

🔹 Advance & Share
41. Learn advanced AI topics
42. Follow latest AI trends
43. Attend AI events online
44. Join AI communities
45. Earn AI certifications

🔹 Final Steps
46. Read AI expert blogs
47. Watch AI tutorials online
48. Pick a focus area
49. Combine AI with other fields
50. YOU ARE READY – Teach & share your AI knowledge!

💬 Double Tap ♥️ For More!

🖥 Free Courses on Large Language Models

▪ChatGPT Prompt Engineering for Developers

▪LangChain for LLM Application Development

▪Building Systems with the ChatGPT API

▪Google Cloud Generative AI Learning Path

▪Introduction to Large Language Models with Google Cloud

▪LLM University

▪Full Stack LLM Bootcamp

#ai #generativeai

✅ Top Artificial Intelligence Projects That Strengthen Your Resume 🤖💼

1. Chatbot Assistant
→ Build a conversational AI using Python and libraries like NLTK or Rasa
→ Add features for intent recognition, responses, and integration with APIs

2. Fake News Detection System
→ Train a model with scikit-learn or TensorFlow on text datasets
→ Implement classification for real-time news verification and accuracy reports

3. Image Recognition App
→ Use CNNs with Keras to classify images (e.g., objects or faces)
→ Add deployment via Flask for web-based uploads and predictions

4. Sentiment Analysis Tool
→ Analyze text from reviews or social media using NLP techniques
→ Visualize results with dashboards showing positive/negative trends

5. Recommendation Engine
→ Develop collaborative filtering with Surprise or TensorFlow Recommenders
→ Simulate user preferences for movies, products, or music suggestions

6. AI-Powered Resume Screener
→ Create an NLP model to parse and score resumes against job descriptions
→ Include ranking and keyword matching for HR automation

7. Predictive Healthcare Analyzer
→ Build a model to forecast disease risks using datasets like UCI ML
→ Incorporate features for data visualization and ethical bias checks

Tips:
⦁ Use frameworks like TensorFlow, PyTorch, or Hugging Face for efficiency
⦁ Document with Jupyter notebooks and host on GitHub for visibility
⦁ Focus on ethics, evaluation metrics, and real-world deployment

💬 Tap ❤️ for more!

🎯 Top 7 In-Demand AI Skills to Learn in 2025 🤖📚

1️⃣ Machine Learning Algorithms
▶️ Learn supervised and unsupervised models
▶️ Key: Linear Regression, Decision Trees, K-Means, SVM

2️⃣ Deep Learning
▶️ Tools: TensorFlow, PyTorch, Keras
▶️ Topics: Neural Networks, CNNs, RNNs, GANs

3️⃣ Natural Language Processing (NLP)
▶️ Tasks: Text classification, NER, Sentiment analysis
▶️ Tools: spaCy, NLTK, Hugging Face Transformers

4️⃣ Generative AI
▶️ Work with LLMs like GPT, Claude, Gemini
▶️ Build apps using RAG, LangChain, OpenAI API

5️⃣ Data Handling & Preprocessing
▶️ Use pandas, NumPy for wrangling data
▶️ Skills: Data cleaning, feature engineering, pipelines

6️⃣ MLOps & Model Deployment
▶️ Tools: Docker, MLflow, FastAPI, Streamlit
▶️ Deploy models on cloud platforms like AWS/GCP

7️⃣ AI Ethics & Responsible AI
▶️ Understand bias, fairness, transparency
▶️ Follow AI safety best practices

💡 Bonus: Stay updated via arXiv, Papers with Code, and AI communities

💬 Tap ❤️ for more!

Artificial Intelligence isn't easy!

It’s the cutting-edge field that enables machines to think, learn, and act like humans.

To truly master Artificial Intelligence, focus on these key areas:

0. Understanding AI Fundamentals: Learn the basic concepts of AI, including search algorithms, knowledge representation, and decision trees.


1. Mastering Machine Learning: Since ML is a core part of AI, dive into supervised, unsupervised, and reinforcement learning techniques.


2. Exploring Deep Learning: Learn neural networks, CNNs, RNNs, and GANs to handle tasks like image recognition, NLP, and generative models.


3. Working with Natural Language Processing (NLP): Understand how machines process human language for tasks like sentiment analysis, translation, and chatbots.


4. Learning Reinforcement Learning: Study how agents learn by interacting with environments to maximize rewards (e.g., in gaming or robotics).


5. Building AI Models: Use popular frameworks like TensorFlow, PyTorch, and Keras to build, train, and evaluate your AI models.


6. Ethics and Bias in AI: Understand the ethical considerations and challenges of implementing AI responsibly, including fairness, transparency, and bias.


7. Computer Vision: Master image processing techniques, object detection, and recognition algorithms for AI-powered visual applications.


8. AI for Robotics: Learn how AI helps robots navigate, sense, and interact with the physical world.


9. Staying Updated with AI Research: AI is an ever-evolving field—stay on top of cutting-edge advancements, papers, and new algorithms.



Artificial Intelligence is a multidisciplinary field that blends computer science, mathematics, and creativity.

💡 Embrace the journey of learning and building systems that can reason, understand, and adapt.

⏳ With dedication, hands-on practice, and continuous learning, you’ll contribute to shaping the future of intelligent systems!

Data Science & Machine Learning Resources: https://topmate.io/coding/914624

Credits: https://t.me/datasciencefun

Like if you need similar content 😄👍

Hope this helps you 😊

#ai #datascience

✅ AI Fundamentals You Should Know 🤖📘

1️⃣ What is AI?
⦁ AI (Artificial Intelligence) is the simulation of human intelligence by machines
⦁ It includes learning, reasoning, problem-solving, perception, and language understanding

2️⃣ Types of AI
⦁ Narrow AI: Performs one specific task (e.g., Siri, ChatGPT)
⦁ General AI: Can perform any intellectual task a human can (still theoretical)
⦁ Super AI: Hypothetical AI with human-level consciousness

3️⃣ Key Domains in AI
⦁ Machine Learning (ML): Systems learn from data
⦁ Natural Language Processing (NLP): Machines understand human language
⦁ Computer Vision: Machines interpret visual data
⦁ Robotics: AI + hardware to automate physical tasks
⦁ Expert Systems: AI-based decision-making systems

4️⃣ AI vs ML vs DL
⦁ AI: The broad concept
⦁ ML: Subset of AI, learns from data
⦁ DL: Subset of ML using neural networks

5️⃣ Machine Learning Categories
⦁ Supervised Learning – Labeled data (e.g., spam detection)
⦁ Unsupervised Learning – Unlabeled data (e.g., customer segmentation)
⦁ Reinforcement Learning – Reward-based learning (e.g., games, robotics)

6️⃣ Popular AI Algorithms
⦁ Decision Trees
⦁ Naive Bayes
⦁ Support Vector Machines
⦁ K-Means Clustering
⦁ Neural Networks

7️⃣ Required Skills for AI
⦁ Python Programming
⦁ Math: Linear Algebra, Probability, Calculus
⦁ Data Handling: Pandas, NumPy
⦁ Libraries: Scikit-learn, TensorFlow, PyTorch
⦁ Problem-solving and critical thinking

8️⃣ Real-World Applications
⦁ Chatbots and virtual assistants
⦁ Fraud detection
⦁ Face recognition
⦁ Personalized recommendations
⦁ Medical diagnostics

💬 Double Tap ❤️ For More

✅ Top Projects Every Data Science Learner Should Build 📂🧠

1️⃣ Exploratory Data Analysis (EDA)
⦁ Dataset: Titanic, Iris, or any public dataset
⦁ Skills: Data cleaning, visualization, correlation analysis

2️⃣ Sales Forecasting Model
⦁ Use time-series data
⦁ Learn ARIMA, Prophet, or LSTM models
⦁ Predict future sales or demand

3️⃣ Customer Segmentation
⦁ Use clustering (K-Means, DBSCAN)
⦁ Segment customers based on behavior or demographics
⦁ Useful in marketing and personalization

4️⃣ Movie Recommendation System
⦁ Use collaborative filtering or content-based models
⦁ Dataset: MovieLens
⦁ Deploy using Streamlit or Flask

5️⃣ Churn Prediction Model
⦁ Dataset: Telecom or SaaS customer data
⦁ Apply classification (Logistic Regression, XGBoost)
⦁ Help businesses retain users

6️⃣ NLP Project – Sentiment Analysis
⦁ Use product reviews or tweets
⦁ Preprocess text, apply TF-IDF or embeddings
⦁ Classify sentiment using SVM or LSTM

7️⃣ Resume Parser
⦁ Use NLP to extract structured info from resumes
⦁ Identify skills, experience, education
⦁ Use Spacy, Regex, and Pandas

8️⃣ Credit Risk Scoring
⦁ Predict if loan applicants are risky or safe
⦁ Use logistic regression or tree-based models
⦁ Balance accuracy and fairness

9️⃣ Data Dashboard
⦁ Tool: Power BI, Tableau, or Dash
⦁ Visualize KPIs, trends, and business metrics
⦁ Link with real-time or mock data

🔟 Deploy ML Model
⦁ Pick any ML model
⦁ Deploy on Heroku or Render using Flask
⦁ Add a basic frontend for input-output

💬 Tap ❤️ for more!

✅ Top Mistakes to Avoid When Learning Artificial Intelligence 🤖⚠️

1️⃣ Starting Directly with Deep Learning
Jumping into Deep Learning before mastering basics like machine learning fundamentals and math can be overwhelming and inefficient, especially with smaller datasets.

2️⃣ Using Biased or Influenced AI Models
Relying on biased data leads to unfair, inaccurate AI predictions. Always clean and ensure diverse, representative datasets.

3️⃣ Mugging Up Theory Without Practice
Memorizing AI concepts without practical hands-on coding and experimenting slows deep understanding and problem-solving skills.

4️⃣ Rushing Through Learning Steps
Trying to learn everything too fast causes confusion. Build foundation step-by-step, validating what you learn against real data problems.

5️⃣ Ignoring Data Quality and Preprocessing
Ignoring data preprocessing ruins model performance, no matter how advanced the algorithm is. Data is key in AI success.

💬 Tap ❤️ for more!

✅ 30 AI Terms Explained....

⌨️ Grammar Correction using Python

Useful websites to practice and enhance your data analytics skills
👇👇

1. Python
http://learnpython.org
http://www.pythonchallenge.com/

2. SQL
https://www.sql-practice.com/
https://leetcode.com/problemset/database/

3. Excel
https://excel-practice-online.com/

4. Power BI
https://www.workout-wednesday.com/power-bi-challenges/

5. Quiz and Interview Questions
https://t.me/sqlspecialist

Haven't shared lot of resources to avoid too much distraction

Just focus on the basics, practice learnings and work on building projects to improve your skills. Thats the best way to learn in my opinion 😄

Join @free4unow_backup for more free courses

ENJOY LEARNING 👍👍

I realized that in the digital world what matters most is my mindset. The industry is not failing sometimes I am the reason behind my own failure.

When I look around and see so many people succeeding.. it becomes clear that the opportunity is real.

So instead of saying the industry is wrong, or this skill is not for me,.... I need to accept that I must improve myself.

Consistency, discipline, and the right attitude are not optional they are essential.

I realized that success comes when I stop blaming the outside world and start working on becoming the version of myself that actually fits the industry....this is th key to win in anything don't be a blamer be a learner✌️✌️✌️

💡 Top 16 Agentic AI Terms

Agentic AI isn’t just a buzzword — it’s a shift.
From reasoning and planning to autonomy and collaboration, these are the key concepts shaping how AI systems think, act, and work together.

Here’s your cheat sheet:
- Agentic AI
- LLMs
- Autonomous Agents
- Multi-Agent Systems
- MCP (Model Context Protocol)
- RAG (Retrieval-Augmented Generation)
- A2A (Agent-to-Agent Protocol)
- Tool Use Agents
- Action Orchestration
- Memory-Augmented Agents
- Reasoning & Planning Agents
- Autonomous Decision Making
- Human-in-the-Loop
- Agent Framework
- Guardrails
- Tool Calling

We’re entering the era where AI doesn’t just respond it reasons, collaborates, and acts.

If you work in AI, product, or data, it’s time to get fluent in this new language.

🚀 Coding Projects & Ideas 💻

Inspire your next portfolio project — from beginner to pro!

🏗️ Beginner-Friendly Projects

1️⃣ To-Do List App – Create tasks, mark as done, store in browser.
2️⃣ Weather App – Fetch live weather data using a public API.
3️⃣ Unit Converter – Convert currencies, length, or weight.
4️⃣ Personal Portfolio Website – Showcase skills, projects & resume.
5️⃣ Calculator App – Build a clean UI for basic math operations.

⚙️ Intermediate Projects

6️⃣ Chatbot with AI – Use NLP libraries to answer user queries.
7️⃣ Stock Market Tracker – Real-time graphs & stock performance.
8️⃣ Expense Tracker – Manage budgets & visualize spending.
9️⃣ Image Classifier (ML) – Classify objects using pre-trained models.
🔟 E-Commerce Website – Product catalog, cart, payment gateway.

🚀 Advanced Projects

1️⃣1️⃣ Blockchain Voting System – Decentralized & tamper-proof elections.
1️⃣2️⃣ Social Media Analytics Dashboard – Analyze engagement, reach & sentiment.
1️⃣3️⃣ AI Code Assistant – Suggest code improvements or detect bugs.
1️⃣4️⃣ IoT Smart Home App – Control devices using sensors and Raspberry Pi.
1️⃣5️⃣ AR/VR Simulation – Build immersive learning or game experiences.

💡 Tip: Build in public. Share your process on GitHub, LinkedIn & Twitter.

🔥 React ❤️ for more project ideas!

✅ Top Artificial Intelligence Concepts You Should Know 🤖🧠

🔹 1. Natural Language Processing (NLP)
Use Case: Chatbots, language translation
→ Enables machines to understand and generate human language.

🔹 2. Computer Vision
Use Case: Face recognition, self-driving cars
→ Allows machines to "see" and interpret visual data.

🔹 3. Machine Learning (ML)
Use Case: Predictive analytics, spam filtering
→ AI learns patterns from data to make decisions without explicit programming.

🔹 4. Deep Learning
Use Case: Voice assistants, image recognition
→ A type of ML using neural networks with many layers for complex tasks.

🔹 5. Reinforcement Learning
Use Case: Game AI, robotics
→ AI learns by interacting with the environment and receiving feedback.

🔹 6. Generative AI
Use Case: Text, image, and music generation
→ Models like ChatGPT or DALL·E create human-like content.

🔹 7. Expert Systems
Use Case: Medical diagnosis, legal advice
→ AI systems that mimic decision-making of human experts.

🔹 8. Speech Recognition
Use Case: Voice search, virtual assistants
→ Converts spoken language into text.

🔹 9. AI Ethics
Use Case: Bias detection, fair AI systems
→ Ensures responsible and transparent AI usage.

🔹 10. Robotic Process Automation (RPA)
Use Case: Automating repetitive office tasks
→ Uses AI to handle rule-based digital tasks efficiently.

💡 Learn these concepts to understand how AI is transforming industries!

💬 Tap ❤️ for more!

AI easily interprets information in simple requests, but if input is very long and complex, model may misunderstand.

To avoid this, try adding structure to prompt and make response of AI more predictable and clear.

How to structure a prompt?

The creators of neural networks suggest using special markup that the AI understands. These can be:

☞ Markdown, a text formatting language. For prompts, you can use bulleted and numbered lists, as well as the # sign, which in Markdown signifies different levels of headings and, in the prompt, defines the hierarchy of tasks.

Task
Plan a birthday party for a company of 8 people.

Restrictions
- Budget: 10,000 rubles 
- Location: at home 
- There are vegetarians among the guests

What should be in the plan?
1. Menu
- Main dishes 
- Snacks 
- Drinks 

2. Entertainment 
- Games 
- Music 
- Activities 

3. Timing of the event

☞ XML tags that indicate the boundaries of any text element. The beginning and end of the element are marked with <tag> and </tag>, and the tags themselves can be any.

<goal>Create a weekly menu for a family of 3 people</goal>

    <restrictions>
        <budget>10,000 rubles</budget>
        <preferences>More vegetables, minimum fried food, soup every day</preferences>
        <exclude>Mushrooms, nuts, seafood, honey</exclude>
    </restrictions>

    <format>
        <meals>breakfast, lunch, dinner, snack</meals>
        <description>A detailed recipe for each dish with a list of ingredients</description>
    </format>

☞ JSON, a data structuring standard that allows you to mark up any information in the prompt with simple syntax.

{
  "task": "Make a shopping list for the week",
  "parameters": {
    "number_of_people": 2,
    "preferences": ["vegetarian", "minimum sugar"],
    "budget": "up to 10,000 rubles"
  },
  "categories": [
    "vegetables and fruits",
    "cereals and pasta",
    "dairy products",
    "drinks",
    "other"
  ],
  "format_of_answer": {
    "type": "list",
    "group_by_categories": true
  }
>

It seems that markup is complicated so you can show your prompt to the AI and ask it to add markup itself without changing the essence.