If you’ve heard people talking about Generative AI or other AI tools and you’re curious about what they are, you’re in the right place. Generative AI isn’t just a trendy term — it’s actually changing the way we work, create things, and use technology every day.
I’ve put together the 10 most important questions that will give you a solid foundation in Generative AI.
- What’s the Difference Between Generative AI and Discriminative AI?** Imagine you’re at an art gallery.
Discriminative AI is like an art expert. It looks at a painting and says, “This is by Van Gogh” or “This is a Picasso.” It’s good at recognizing and telling the difference between things.
Discriminative AI: “Is this a cat or a dog?” → It’s all about choosing or classifying.
Generative AI is like the artist. It doesn’t just look at art — it creates it! It can make new paintings, write stories, compose music, or even write code.
Generative AI: “Draw a cat wearing a superhero cape.” → It’s all about creating something new.
- How Are Large Language Models Trained, and What Challenges Do They Face?
Note:-An LLM is a type of artificial intelligence (AI) that is trained to understand and generate human language. It’s like a super smart chatbot that can read, write, summarize, translate, answer questions, and more.
🧠 The Training Process
Step 1: Collecting Data
The model reads tons of text — books, websites, articles — billions of pages!
Imagine every book in every library, all read by a super-fast learner.
Step 2: Learning Patterns
The model studies how words go together.
For example, it learns that:
“The cat sat on the…” is often followed by “mat” or “chair.”
It figures out grammar, common phrases, and how people usually talk or write.
Step 3: Getting Better (Fine-Tuning)
After the first learning, humans help the model improve.
Like a tutor giving feedback so it answers more clearly, politely, and correctly.
⚠️ The Big Challenges
💻 Lots of Computing Power
It takes super-powerful computers and a lot of electricity — sometimes enough to power a small city!
📚 Good vs Bad Data
If the model reads bad or biased info, it can learn the wrong things. So, data quality really matters.
📏 Size Matters (But Costs More)
Bigger models are often smarter, but they take way more time, money, and energy to build and use.
3.What are some popular generative AI models used for natural language generation, and what are their key features?
Note:-NLP stands for Natural Language Processing.
It is a field of Artificial Intelligence (AI) that focuses on how computers can understand, interpret, and generate human language (like English, Hindi, etc.).
Meet the Stars of the Generative AI World
GPT Series (by OpenAI)
What it does: Writes and talks like a human. Can help with writing, chatting, coding, and more.
BERT (by Google)
What it does: Understands the meaning of words by reading the full sentence.
T5 (by Google)
What it does: Turns all language tasks into a “text-in, text-out” job.
Claude (by Anthropic)
What it does: Aims to be helpful, safe, and honest.
4.Explain the concept of transfer learning as applied to large language models.
Transfer learning is like learning tennis after you already know how to play badminton. You don’t have to start from zero — you use the skills you already have.
How It Works in AI:
First, a model learns from a big, general dataset (like learning basic language skills).
Next, that model is adjusted (fine-tuned) to do a specific job — like understanding medical words.
Result? The model does the new job much better than if you trained it from scratch.
Why It’s a Game-Changer:
Saves Time: Training can take hours or days, not months.
More Access: Smaller teams can create smart AI without huge resources.
Better Results: The model starts with good knowledge, so it performs better.
Think of It Like This:
You don’t teach every doctor how to talk from birth. You teach them language first, then medical terms. That’s much faster and smarter
- Discuss the concept of perplexity in the context of evaluating language models, and its significance in generative AI.
Perplexity sounds complicated, but it’s actually a simple concept. Think of it as a measure of how “surprised” an AI model is by what comes next in a sentence.
Understanding Perplexity
Low Perplexity: The model is confident and unsurprised
Example: After “The sun rises in the…” the model confidently predicts “east”
High Perplexity: The model is confused and uncertain
Example: After “The quantum flux capacitor…” the model has no idea what comes next
Why It Matters
Model Evaluation: Lower perplexity generally means better performance
Comparison Tool: Helps researchers compare different models objectively
Quality Indicator: Shows how well a model understands language patterns
It’s like grading a student’s reading comprehension — the less confused they are by the text, the better they understand it.
6.How do generative AI models like GPT-3 and BERT utilize attention mechanisms in their architecture?
Attention mechanisms are one of the most important breakthroughs in AI. They help models figure out what to focus on, just like how you focus on specific words when reading.
How Attention Works
Imagine you’re reading the sentence: “The cat that lived in the house with the red door was very friendly.”
When processing “was very friendly,” the attention mechanism helps the model focus on “cat” rather than getting confused by “house” or “door.”
In GPT and BERT
GPT (Generative Pre-trained Transformer):
Uses “self-attention” to understand relationships between words.
Looks at previous words to predict the next one.
Like reading a story and using context to guess what happens next.
BERT (Bidirectional Encoder Representations from Transformers):
Uses attention to look at words from both directions.
Considers the full context before making decisions.
Like reading a sentence completely before understanding its meaning.
Why It’s Revolutionary
Better Context Understanding: Models can handle longer, more complex text
Improved Performance: Attention mechanisms dramatically improved AI capabilities
Interpretability: We can sometimes see what the model is “paying attention to”
7.Explain the challenges of bias and fairness in generative AI and large language models, and strategies to mitigate them.
Bias in AI is one of the biggest challenges we face today. Let’s break it down:
Types of Bias in AI
a) Training Data Bias
If the data used to teach the AI mostly includes certain groups, the AI will learn those patterns.
📌 Example: If most CEOs in the data are men, the AI might think only men can be leaders.
b) Algorithmic Bias
Sometimes the way the AI is built can lead to unfair decisions.
📌 Example: A hiring AI might accidentally treat some people unfairly based on age, gender, or background.
c) Confirmation Bias
The AI might repeat and strengthen stereotypes it has learned.
📌 Example: Linking certain jobs to only one gender or race.
How to Reduce Bias in AI
Use Diverse Data
Include examples from all kinds of people
Make sure underrepresented groups are included
Test for Bias
Regularly check if the AI is making fair decisions
Get help from diverse teams to spot problems
Build Fair Algorithms
Add rules that help keep the system fair
Use tools that check the fairness of decisions
Keep Monitoring
Watch how the AI behaves after it’s launched
Be ready to fix any issues that show up
Be Transparent
Make it clear how the AI makes decisions
Let people step in when needed
The goal:
We may not remove all bias, but we can reduce the harmful effects and make AI as fair as possible for everyone.
8.What are some limitations or drawbacks of current generative AI and large language models, and potential areas for improvement?
🔴 Limitations of Generative AI & LLMs
Hallucinations — AI can generate false or misleading information.
Bias in Outputs — May reinforce harmful stereotypes from training data.
Lack of True Understanding — Models don’t actually “understand” content like humans.
🟢 Areas for Improvement
Better Fact-Checking — Connect models with reliable, real-time data sources.
Fairness & Safety Controls — Reduce bias through diverse data and testing.
Improved Reasoning — Teach models to follow logical steps, not just patterns.
And the last 2 questions are for you to explore:
Latent Space and Diffusion Models — take some time to learn what they mean and how they work.
If you’d like a quick example and an easy way to build an AI agent, don’t forget to comment! I’ll be sharing a simple, step-by-step guide with a foolproof plan.
Follow me please for such more intersecting things.
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